Seedefy, a Singapore-registered company, began with a proposition that resonated with the region’s startup ecosystem. It positioned itself as a bridge between early-stage founders and capital, promising structure, access, and a clearer path through the fragmented world of seed funding. In a market where many first-time founders struggle to navigate investors, accelerators, and legal complexity, the narrative proved compelling.

For a period, that positioning gained traction. Seedefy attracted attention, partnerships, and a growing community of founders who viewed it as an entry point into the funding landscape. Its pitch aligned with Southeast Asia’s appetite for platforms that simplify complexity and lower barriers to entry. Momentum followed, as is often the case in early-stage ventures.

That trajectory later shifted, and the shift was comparatively rapid.

When momentum masks fragility

As with many young startups, Seedefy’s perceived growth appeared to advance more quickly than its underlying foundations. Expansion of scope progressed faster than the development of internal controls. Governance arrangements, incentive structures, and execution capacity became increasingly difficult for external stakeholders to evaluate, even as visibility increased.

This sequence is familiar. Early traction tends to foster confidence, which in turn reduces scrutiny. Investors and partners extrapolate short-term signals into assumptions of durability. In Seedefy’s case, the narrative retained credibility even as signs of structural strain became more apparent.

When doubts began to surface more broadly, they translated into erosion rather than prolonged decline. Confidence weakened. Relationships became strained. The business model showed limited resilience under closer examination. What followed was not a dramatic collapse, but a relatively swift loss of relevance. The platform receded from the centre of the ecosystem it had sought to organise.

Also Read: Why due diligence matters, especially when investing in early-stage startups

Due diligence rarely fails loudly

Seedefy’s trajectory appears less rooted in overt misconduct than in layered assumptions. Investors anticipated governance would mature over time. Founders expected scale to address early gaps. Partners inferred alignment where documentation, incentives, and regulatory positioning were not always clearly articulated.

One element proved particularly consequential. Certain aspects of the model operated close to regulated financial and crypto-adjacent activities, in a context where licensing requirements and regulatory boundaries were not always clearly delineated. While this point rarely dominated discussion, it increased exposure once scrutiny intensified.

Due diligence often falters in this understated manner. The issue is not missing documentation, but deferred questions. Who ultimately holds decision-making authority? How conflicts are resolved. How regulatory considerations are managed as models evolve. How downside scenarios are addressed in practice.

In early-stage investing, such questions are frequently postponed. Speed, access, and fear of missing out tend to prevail. The cost typically emerges later.

The Founder remains the central risk

What Seedefy illustrates is a reality many investors acknowledge privately but underweight in practice. The founder remains the most influential variable in any early-stage company.

Markets evolve. Products pivot. Strategies adjust. Behavioural patterns, decision-making styles, and approaches to accountability tend to display greater continuity.

Effective due diligence, therefore, extends beyond pitch decks and data rooms. It involves examining a founder’s operating history with care, including discussions with former colleagues, employees, contractors, and prior investors. The objective is not to seek consensus, but to identify consistency in how pressure was handled, conflicts addressed, and responsibility assumed when outcomes disappointed.

Such conversations rarely yield definitive judgments. They do, however, add depth. They transform narrative into context. For investors, that additional dimension is often decisive.

Integrity, responsibility, and how founders exit matters

The contrast becomes clearer when viewed alongside how other founders have handled comparable outcomes. In recent years, a number of early-stage founders have chosen a more explicit approach when ventures failed to meet expectations. They communicated directly with investors and partners, acknowledged misjudgements, and remained accessible after operations ceased.

A recent example shared publicly by a founder illustrates this approach clearly, documenting the decision to wind down a startup with transparency, personal accountability, and continued engagement with stakeholders even after commercial prospects had ended.

Also Read: ‘Due diligence is like dating before the long-term marriage’: Accion Venture Lab’s Paolo Limcaoco

In some cases, founders publish post-mortems. In others, they remain reachable long after operations have stopped. These actions rarely alter financial outcomes, but they materially shape trust. Investors are left informed rather than uncertain. Employees and partners receive closure rather than silence. The failure of the venture does not extend into a failure of responsibility.

This distinction matters. In early-stage companies, failure itself is rarely disqualifying. How founders behave once momentum breaks often proves more revealing than how they behave during periods of growth. Transparency under pressure, willingness to engage when prospects dim, and respect for stakeholder relationships tend to persist into future ventures.

From a due diligence perspective, this underscores the value of examining not only how founders build, but how they unwind. Past shutdowns, difficult chapters, and public accountability often provide more signal than success stories alone.

The asymmetry of startup risk

Early-stage investing remains defined by asymmetry. Upside attracts attention. Downside determines outcomes.

Seedefy demonstrates how non-financial risks can shape results. Governance risk. Execution risk. Regulatory exposure. Alignment risk. These factors resist simple modelling, yet they frequently influence survival.

For angel investors and early backers, narrative proximity can feel reassuring. Distance and scepticism tend to offer greater protection.

A broader warning for the ecosystem

Southeast Asia’s startup ecosystem continues to mature. Capital is more accessible. Structures appear more sophisticated. The fundamentals of risk remain unchanged.

Platforms such as Seedefy emerge because they address genuine pain points. Their failure does not negate those problems. It reinforces the importance of discipline on the investment side of the table.

Notably, Seedefy did not conclude with a clearly communicated endpoint. Activity diminished. Public communication subsided. The company appeared to wind down without a formal announcement or resolution. For investors, that absence of closure carried its own implications.

Due diligence is not an administrative exercise. It sits at the core of early-stage investing. When treated as secondary, outcomes tend to converge toward disappointment.

What investors should take away

The rise and fall of Seedefy offers a restrained but instructive reminder. Early momentum does not ensure durability. Visibility does not guarantee governance. Access does not confer protection.

Investors who remain active over time tend to develop a preference for structure, context, and downside analysis. They ask fewer aspirational questions and more operational ones. In early-stage investing, those questions often separate informed risk from avoidable loss.

The cost of overlooking them is rarely immediate. When it materialises, it is usually conclusive.

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Artificial intelligence (AI) is no longer a futuristic concept reserved for tech giants and multinationals. For small and medium-sized enterprises (SMEs), particularly in Southeast Asia (SEA), AI is increasingly becoming a practical tool that offers immediate business value.

William Smith, Head of Mid-Market Sales, Asia at Zoom, offers an insider’s perspective on how AI is already transforming the region’s SMEs and how these companies can strategically leverage it to accelerate growth while controlling costs.

According to Smith, most SMEs he engages with share two core business priorities: driving explosive growth and managing tight budgets. “Resources are finite, whether that is manpower or capital … SMEs are looking to AI as a force multiplier that enables them to scale rapidly without proportionally increasing headcount.”

AI allows SMEs to reallocate human resources to more strategic, value-added tasks while automating time-consuming operational work. This dual benefit is crucial for businesses trying to penetrate new markets or expand beyond their home countries.

“Every country in SEA has its own culture and language, making regional expansion particularly complex,” Smith notes. “AI can help bridge these gaps by simplifying cross-border communication and improving operational efficiency.”

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

Barriers to AI adoption: Misconceptions and trust issues

Despite AI’s immense potential, many SMEs hesitate to adopt it fully. Smith identifies three main concerns: perceived cost, data reliability, and workforce integration.

Firstly, many SMEs believe AI solutions are prohibitively expensive. Smith argues this is often a misconception driven by legacy pricing models. “Some companies charge US$20 to US$30 per AI licence, which adds up quickly for SMEs. In contrast, platforms like Zoom bundle AI features into existing subscriptions at no extra cost, making AI accessible even for smaller businesses.”

Secondly, trust in AI-generated data remains a significant hurdle. SMEs worry whether the outputs are reliable and actionable. “It’s not just about security, but about the trustworthiness of the information they receive,” says Smith.

He emphasises the importance of SMEs selecting AI platforms that adopt robust data models, such as Zoom’s federated approach, which aggregates information from multiple verified sources to ensure accuracy.

Finally, the complexity of integration often intimidates SMEs. Many believe that adopting AI requires extensive feasibility studies, proof-of-concept trials, and technical expertise that they may not possess. Smith counters that AI adoption doesn’t have to be complex if businesses choose platforms that seamlessly integrate AI into their existing workflows.

“We weave AI directly into our platform, allowing users to see immediate ROI without complicated implementation.”

Smith also shares several compelling examples of how SMEs harness AI to achieve tangible business outcomes.

Also Read: Your AI product may fall, and how you can save it

One such company is an education provider in Hong Kong. Before adopting AI, they employed five staff members solely to attend virtual classes and take notes manually. “With Zoom’s AI Companion generating automatic meeting summaries in over 30 languages, those employees are now focused on curriculum development and student engagement, significantly increasing their productivity and business impact,” Smith recounts.

Another case involves Nexusguard, a Singapore-based company with a global footprint. Facing challenges around multilingual communication across various regions, Nexusguard leveraged Zoom’s translated captions to enable smooth internal and external communications without the need for costly language specialists.

“They saved on staffing costs while enhancing their ability to expand into new markets quickly and professionally,” Smith says.

A strategic framework for AI adoption

For SMEs contemplating their AI journey, Smith recommends a focused, outcome-driven approach. “Start by identifying your business goals and then work backwards to pinpoint gaps AI can help address,” he advises.

Internally, SMEs should examine whether their employees spend excessive time on low-value tasks that AI can automate. Externally, they should evaluate whether customer experience and service levels could be improved with AI-powered tools such as chatbots or virtual agents.

“AI should not be seen as a one-size-fits-all solution,” Smith stresses. “It’s about identifying specific use cases where AI can relieve pressure on limited resources while enhancing customer satisfaction.”

While the benefits are clear, Smith cautions SMEs against rushing into AI adoption without a clear purpose. “AI means different things to different industries. A retail business facing customer service bottlenecks will require different AI solutions than a manufacturing firm focused on supply chain optimisation.”

Also Read: SEA mobile gaming surges: 1.93B installs and growing global influence

SMEs should take time to understand what AI means within the context of their own operations. “Do your due diligence,” Smith advises. “Make sure any solution you adopt aligns with your industry needs, excites your employees, and delivers measurable returns.”

Ultimately, Smith believes that AI’s true role is to enhance human potential rather than replace it. “The human remains at the centre of everything … AI should empower SMEs by freeing up their people to focus on strategic initiatives that drive sustainable growth.”

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Lyra Reyes also contributed to this article.

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At this year’s Asia Tech x Singapore, the region’s premier flagship technology event, a standout moment came from a fringe speaking panel titled, “Unlocking Southeast Asia’s AI Potential: From Multilingual Models to Market Impact”. As I listened to Temus’ AI Leader, Matt Johnson and the other panellists, three personal reflections took shape, each pointing to what it will take for ASEAN to secure its place in the global AI value chain.

ASEAN needs an interoperable AI production base

Today, all ten ASEAN states have published national AI strategies. Singapore’s NAIS 2.0 pushes practical, sector-level AI. Malaysia’s MADANI AI Framework emphasises ethical and inclusive development. Indonesia’s BRAIN project links AI to economic modernisation.

But taken together, these efforts reveal a structural problem: Fragmentation. As of 2025, ASEAN lacks aligned data protection regimes, shared compute infrastructure, or interoperable standards for AI. These gaps limit scalability and regional trust.

Against this backdrop, Europe’s aspiration for a Silicon Schengen offers a useful analogy. It envisions seamless cross-border collaboration in the semiconductor industry, mirroring how the Schengen Area enables the free movement of people. While there are no formal members, the idea draws from real initiatives like the Silicon Europe Alliance — a robust network of 12 leading technology clusters across the continent.

These clusters, focused on microelectronics, photonics, and software, bring together over 2,500 companies and research institutions—from nimble SMEs to global giants. Key hubs include Silicon Saxony in Germany, Minalogic in France, and DSP Valley in Belgium, each playing a vital role in Europe’s drive for a more integrated semiconductor ecosystem.

Southeast Asia, with its rich diversity and geopolitical complexity, doesn’t need to replicate Europe’s model, but the spirit behind Silicon Schengen is worth emulating. If you’ll allow me a little creative license, imagine an ASEAN AI Archipelago: a connected chain of digital ecosystems stretching from Jakarta to Hanoi, Singapore to Manila, giving member states the chance to shape a model uniquely their own. One not defined by uniformity, but grounded in interoperability, inclusivity, and regional relevance.

The recently-concluded inaugural ASEAN-GCC-China Summit underscored the centrality of regional cooperation and ASEAN’s growing potential as a production hub, linking Gulf capital with China’s technological strengths. It signalled the beginning of a new trilateral effort to build more resilient and diversified supply chains. Amid a fracturing global trade system, could the idea of an ASEAN AI Archipelago serve as a foundational layer in a new architecture of trade, innovation, and production for regional blocs?

Localisation must be a design principle

Before ASEAN can build a unified, AI-enabled production base, it must first secure two essentials: local relevance and workforce readiness. AI in this region doesn’t thrive on scale or technical sophistication alone—it succeeds when it is trusted, usable, and adapted to the rhythms of local businesses and communities. 

This is what makes SEA-LION, Southeast Asia’s first multilingual large language model, so important. Trained on Bahasa Indonesia, Vietnamese, Thai, Tagalog, and English, it was built not only to understand language, but to operate efficiently on local infrastructure, even for smaller businesses. That matters deeply in ASEAN, where SMEs make up 97 per cent of all enterprises and employ over 85 per cent of the region’s workforce. If AI cannot work for SMEs, it cannot work for ASEAN.

Also Read: US tariffs on semiconductors and autos put Malaysia’s trade at risk

The stakes are rising with the emergence of Agentic AI, systems capable of acting autonomously, not just making predictions. These tools promise to transform how work gets done, but their success is far from guaranteed. In Southeast Asia, 75 per cent of failed AI deployments can be traced to poor cultural and operational alignment.

And while 85 per cent of businesses in the region now recognise that localisation, beyond language, into workflows and business norms, is essential, adoption remains uneven. Without addressing these gaps, AI will remain concentrated in large enterprises, bypassing the SME backbone that powers the regional economy. 

Singapore offers a preview of what’s possible when policy, partnership, and capability-building align. In 2023, thousands of local SMEs adopted AI tools, and the national target is to raise this number to 15,000 by 2026. Over 8,000 mid-career professionals have already completed structured AI training through public programs.

At Temus, we’ve contributed to this effort through a strategic partnership with AI Singapore, working directly with local organisations to prepare datasets, implement practical AI tools, and sustain them in real-world environments. Our collaborative programs, including AI for Leaders and the AI Apprenticeship Programme, are designed not just to educate, but to embed lasting capability. By 2025, over 30 AI apprentices had been hosted at Temus, gaining firsthand experience while helping organisations build capacity from within.

AI adoption requires a resilient semiconductor backbone

None of this is possible without chips. ASEAN Semiconductor Market is estimated to reach SG$67 billion by 2032. Singapore contributes 11 per cent of global semiconductor assembly, testing, and packaging (ATP) capacity, while Malaysia adds another 7 per cent. Vietnam and the Philippines are rapidly scaling their capabilities, bolstered by foreign investment and industrial park expansion.

But the real promise of an ASEAN AI Archipelago lies in integration. 

The Johor–Singapore Special Economic Zone (SEZ) exemplifies this complementarity in action. Singapore leads with strengths in IC design, R&D, and advanced manufacturing. Johor, directly across the Causeway, offers scalable land, cost-competitive labour, and logistics infrastructure. Just north of the SEZ, Penang, home to more than 350 electronics firms, including Intel and AMD, adds vital capacity in wafer fabrication, assembly, and back-end testing. 

These nodes are tied together not just by geography, but by policy coherence. Mutual recognition of professional qualifications, streamlined customs procedures, and flexible labour mobility enable cross-border teams and supply chains to function with agility. 

Also Read: Building smart: A tech founder’s guide to the semiconductor supply chain revolution

In a world of rising geopolitical tension and tightening export regimes, ASEAN’s integrated production corridors offer something rare: Optionality. For global chip firms like Nvidia, that could be existential.

Nvidia’s greatest vulnerability is not technological, it’s geopolitical. Its AI chips depend on a complex global supply chain: design and IP from the US; wafers from Taiwan and Japan; EUV lithography from ASML in the Netherlands; fabrication at TSMC; packaging and testing in South Korea, Malaysia, and Vietnam.

Finally, Southeast Asia’s logistics hubs move these high-value chips into data centres and markets around the world. As export controls tighten and chokepoints multiply, Nvidia, and others, must redesign for anti-fragility. That means investing across ASEAN for strategic depth. Partnerships in Singapore, Malaysia, Vietnam, and Thailand offer distributed resilience.

At the same time, encouraging ecosystem partners like TSMC and Samsung to expand their advanced packaging operations across Southeast Asia, investing in local talent and collectively, establishing a semiconductor backbone, or a corridor, if you like. 

Insights from Echelon Singapore 2025

Whether through an ASEAN AI Archipelago or another model of regional integration, Southeast Asia’s role in the global AI value chain must evolve, from serving as a low-cost transit point to becoming a critical node for innovation, production, and resilience.

These themes took centre stage at Echelon Singapore 2025, where I had the privilege of moderating a panel titled “Building in the Semiconductor Age: What Tech Founders Need to Know About Supply Chains, Partnerships, and Strategic Positioning”.

Joined by Teong Wei Tan (Infineon), Chan Yip Pang (Vertex Ventures), and Jinsong Xu (Innowave Tech), we explored how founders across Southeast Asia can ground their innovation efforts in strong semiconductor foundations, strategic partnerships, and a skilled regional workforce.

The discussion reinforced one key takeaway: ASEAN’s future in AI will be defined not just by what we build, but by how we build together.

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In an era where artificial intelligence, automation, and advanced analytics are rapidly transforming the world of work, the conversation in Southeast Asia must shift from “what technology can do” to “how humans can use technology to solve real problems.”

The rise of AI has created both excitement and anxiety. But at its core, AI is a tool—an incredibly powerful one—but still a tool. It is not a new master to bow to.

Our region must not fall into the trap of glorifying technology for its own sake. Instead, we must focus on cultivating human skills that harness and direct technology meaningfully, especially in ways that serve the real challenges of our communities, businesses, and governments.

Across Southeast Asia, countries are investing in digital transformation, smart cities, fintech, and e-government platforms. Yet, many organisations are still hiring for technical know-how without emphasising critical thinking, creativity, empathy, collaboration, and ethical judgment.

These are the very human skills that cannot be easily replicated by machines—and they are essential for ensuring technology serves society, not the other way around.

AI without purpose creates friction

AI is often seen as the latest shiny object. But without a clear use case, it becomes a solution looking for a problem. For example, many Southeast Asian SMEs adopt AI chatbots, only to frustrate customers with rigid, robotic interactions. Why? Because they focus on the technology, not the user experience.

Contrast this with a successful example from Indonesia, where AI-powered mobile apps are helping rural farmers forecast crop yields and access micro-loans. The difference lies in the application: tech that solves a real-world problem, guided by human insight.

To truly leverage AI and other emerging technologies, we need to train our workforce differently. The traditional education system in much of Southeast Asia emphasises rote learning and technical proficiency. While these are important, they must be complemented with project-based learning, interdisciplinary problem-solving, and industry immersion.

Programs that connect students with real business challenges—such as digital marketing for SMEs, or logistics optimisation for rural supply chains—help young people see tech as a means, not an end.

Also Read: The resume is dead: Why 80 per cent of companies fail to hire based on real skills

Singapore, for instance, is beginning to model this shift. Initiatives like SkillsFuture and AI Singapore promote continuous learning and applied AI research that involves industry partnerships. But there is still a long way to go in ensuring these skills reach beyond the tech elite. In Malaysia and the Philippines, where talent is abundant but access to high-quality training is uneven, public-private partnerships can help democratise AI literacy while reinforcing problem-solving skills as the core of any tech deployment.

AI needs human direction

Human-centric hiring means looking beyond the resume. Southeast Asian employers must begin to value traits like adaptability, curiosity, empathy, and storytelling—especially when paired with basic tech fluency.

A developer who can explain the societal impact of their algorithm is more valuable than one who can only write clean code. A healthcare worker who uses digital tools to track patient outcomes while listening compassionately can bridge the human-tech divide in meaningful ways.

So, should we fear AI? Not if we remember that it works best when directed by people who understand the problem, care about the outcome, and ask the right questions. AI can scale our ideas, but it cannot generate purpose. It can detect patterns, but not set values. It can optimise, but not empathise.

The real promise of technology

In conclusion, Southeast Asia’s future does not depend on how many coders we produce, but how many problem-solvers we empower.

Let us shift our focus from hiring for tech, to hiring for human potential in a tech-heavy world. Let us build a workforce that sees AI not as an authority, but as a collaborator in crafting better services, stronger economies, and more inclusive societies.

That is the real promise of technology—and the leadership challenge of our time.

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There has been a growing debate about putting AI data centres into space.

AI needs enormous amounts of power. Space has constant sunlight for solar power. And if launch costs keep falling, maybe it will finally make sense to move the data centres into orbit.

Until recently, this could be dismissed as science fiction. Today, it deserves to be taken seriously — but only if we follow the economics all the way through.

When you do, the answer turns out to be very clear, and very different to what is being discussed.

Why is this question being asked now

This conversation is not driven by AI. It is driven by launch costs.

For most of the space age, lifting large amounts of mass into orbit was prohibitively expensive. That constraint has changed dramatically in the last decade.

Launch costs to low Earth orbit have followed a steep and dramatic decline: from more than US$40,000/kg historically to US$2500–3000/kg today and targeting US$100–300/kg in SpaceX’s new Starship

To stay conservative, this analysis assumes US$150/kg to LEO — not a promise, but no longer a fantasy.

That single shift turns space from an exotic environment into something closer to infrastructure.

The data centre energy stack

To ground this discussion in reality, consider a facility like xAI’s Colossus, operating at roughly 300 MW of continuous power.

The current “best possible” energy stack is a mix of onsite gas turbines, grid connections, a small amount of solar and a few batteries for smoothing

Some of that power is delivered via the grid, some via on-site generation. For a true cost comparison, we can treat the energy stack as if it were fully dedicated to the site.

The cost of building the energy stack is around US$550–1050M

Plus annual maintenance and fuel costs of US$100–180M a year

Gas is not a backup in this model. It is structural.

Also Read: Breaking into the data centre sector: Beyond technical expertise

Why look to space at all?

Because AI needs power at scale, and it needs it to be stable, and we need a route there that doesn’t depend on extracting and burning ever more fossil fuels.

Solar is an obvious solution; however, on Earth, even excellent solar installations deliver only 25–30 per cent of their theoretical output over a year. Solar in orbit benefits from constant sunlight 40 per cent stronger than on the surface of the earth and is effectively firm by default. There is no night, no weather, and no seasonal variation. Once built, it can be 100per cent solar without fuel or large storage.

That single difference is what makes space interesting.

What does a 300 MW space-based solar energy stack weigh?

The cost to get a solar plant in space is the cost per kg we discussed before times the number of kgs it weighs. Modern space-solar designs use ultralight photovoltaic membranes rather than glass-and-steel terrestrial panels. With no wind or gravity, structures can be far lighter.

Consensus estimates a conservative near-term figure of 0.8 kW per kilogram of photovoltaic material is plausible.

At that density, 300 MW requires ~375 tons of panels.

Even in space, you still need structural support, wiring, power electronics, and control systems. These add mass, though far less than on Earth.

Using optimistic but defensible assumptions, non-panel components add roughly one to two times the panel mass.

That puts the total mass required to generate 300 MW in orbit at approximately 750–1,100 tonnes.

At US$150/kg to LEO, and another 15–20 per cent to raise to GEO, it is expensive, but single-time — and crucially, it buys something Earth-based solar cannot: firm power without fuel.

These figures reflect a post-industrialised SBSP cost regime; today, a kind of 300 MW GEO system would cost over a billion dollars, but with repeat builds and learning-curve effects, these ranges are plausibly achievable within ~10–25 years.

Annual operating costs are minimal:

Annual cost of operations and maintenance: US$3–6M / year

No fuel. No price volatility.

Also Read: The AI age is changing the data centre industry – Here’s how Singapore can pivot

What about the data centre?

At this point, now that we know that moving the energy stack to space is feasible, we can look at moving the data centre itself.

This is when the numbers break.

A data centre is not just chips. It also comprises power electronics, cooling systems, structural containment, cabling, and radiation shielding. Even reducing the weight of the structure for zero gravity, we’re looking at a 300 MW AI data centre of 13,000–15,000 tons.

Plugging in our conservative near-term launch costs of ~US$150/kg to LEO, that implies:

• US$1.95–2.25 billion to launch the data centre, before orbital transfer.

And a second factor has to be added: unlike the solar infrastructure, this cost is not one-time.

Chips are replaced every three to five years. That means most of the compute mass would need to be relaunched on that cadence.

No plausible launch-cost trajectory fixes this asymmetry.

That is why putting compute in orbit fails economically — even in a world where space-based energy begins to make sense.

The pivot the numbers force

Once it becomes clear that data centres are too heavy and too short-lived to move economically, the problem reframes itself.

The thing that should move to space is the energy stack.

The thing that should stay on Earth is the computer.

Beaming power is real technology: The basic architecture is straightforward: collect sunlight in space, convert it to microwaves, beam it at low intensity, below that of radar, to a large “rectenna” on Earth, which is a simple large mesh and convert it back to electricity. No exotic physics or speculative materials are required; power beaming has been demonstrated terrestrially and at a small scale in space.

End-to-end efficiency is not 100 per cent. A reasonable near-term assumption is that only about two-thirds of generated power reaches the data centre, which means the orbital solar array must be oversized by roughly 1.5×.

For a 300 MW continuous data-centre load on Earth, that implies ~450 MW of space solar generation, plus transmission hardware.

Also Read: Is Southeast Asia’s data centre boom headed for a PR crisis?

Adding transmission capabilities and increasing the capacity to 450MW changes our costs: (again, after price optimisation, not first of a kind today, which would be two to three times the cost for a prototype)

The logical conclusion

Although the upfront cost of space-based solar is higher, the difference in annual fuel and operating cost is large enough to repay that in a handful of years.

And with no future fuel cost risk.

Why isn’t everyone doing this? Because launch costs only crossed the threshold very recently. Even a 2024 NASA study still assumed Falcon 9–era economics.

And politically, it’s easier to talk about “AI in space” than “power beamed from orbit.”

But narratives follow incentives.

As launch becomes cheap and power demand explodes, the industry will pivot.

Not to data centres in space.

But to something far more powerful: Data centres on Earth, powered by cheap, stable, solar energy from space.

That’s the real solution hiding in plain sight.

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In late November 2025, Soulpower Acquisition Corporation and SWB LLC, the company behind SOUL WORLD BANK, announced a merger agreement that would bring the business public on the NYSE at an estimated $8.1 billion valuation, backed by $5 billion in committed funding, pending shareholder and regulatory approval.  

Soul World Bank was formed in response to a financial system that remains fragmented, border-dependent, and difficult to access for many individuals and businesses operating across markets. The company intends to offer a range of international financial services, built around newer technologies such as artificial intelligence, stablecoins, and tokenization. To make this happen, the company lists technology partners that are designed to make financial services more accessible and efficient for everyday users, rather than only large institutions.

Through this collaboration — alongside partners such as Animoca Brands — NewCampus brings its community and systems building experience into finance, extending the same mission into a larger and more complex arena.

Justin Lafazan, CEO of Soulpower and founder and managing member of SWB, underscored the importance of that partnership, saying, “We are all in on Will Fan and the NewCampus team and could not imagine building SOUL WORLD BANK without them. Our partnership with NewCampus means we are starting with a proven team and in-place network across Southeast Asia.”

NewCampus, an innovation firm based in Singapore, focuses on community building and operational infrastructure for emerging institutions. Its involvement in Soul World Bank reflects a broader recognition that technology alone is no longer sufficient to build complex financial systems. This article examines how ecosystem-building principles developed in one sector can translate effectively across others.

A decade of building communities, now building for finance

NewCampus’ core mission focused on rethinking how leaders in Asia learn, grow, and work together. NewCampus has built over a decade of experience as one of the top “challenger universities in Asia” –  an alternative model to traditional universities that focuses less on formal degrees and more on practical learning, community, and real-world outcomes. Working with over 500 companies worldwide and reaching more than 130,000 learners, NewCampus continues their mission that real progress begins when people are seen fully, not just as account holders, but as workers, parents, builders, and dreamers. 

That experience is now being applied in a new context in an unusual pairing between education and banking. NewCampus has repeatedly built institution-like systems from scratch where participation, trust, and empathy support leadership pipelines and more efficient operational structures, including its partnership with Open Campus to invest in more than 140 edtech companies, impacting over 20 million learners globally.

Their multi-country, stakeholder approach has built a systems-first legacy that applies to old industries that need disruption. While core expertise lies in learning infrastructure and leadership development, NewCampus also boasts an impressive track record building ecosystems that scale.

Soul World Bank is built on the premise that the next chapter of global banking must be built with the communities it serves. These same capabilities – building engaged communities, creating operational infrastructure, and enabling peer networks – are exactly what new financial institutions need.

As CEO Will Fan has shared publicly, “The past decade, I’ve been building a challenger university to reimagine how leaders in Asia learn, grow, and build. Today, I’m bringing those lessons into a new arena: launching a challenger world bank.” He is excited about joining forces with the Lafazan Brothers and Animoca Brands to help build SOUL WORLD BANK calling it the “same mission, bigger playground (and) reshaping access to opportunity.”

More is expected to come as NewCampus helps shape this next chapter of global finance, specifically, one rooted in technology, humanity, and the courage to reimagine what’s possible through new economy banking for frontier markets.

Also read: Bring your most authentic self to the table whether at home or work: Will Fan of NewCampus

An $8.1 billion bet on new economy banking

An $8.1 billion valuation is notable for an Asia-linked institution listing in the United States. Historically, most companies from the region debut on the New York Stock Exchange at significantly smaller sizes, often well below the $1–3 billion range (compared to Singapore Carro’s predicted $3bn valuation last August). In this context, the scale of Soul World Bank’s proposed listing stands out, particularly as it enters the public markets with substantial capital commitments already in place.

The transaction follows a SPAC merger designed to form a new economy financial services group with a global footprint. Based on publicly disclosed information, the venture combines an $8.1 billion valuation with a $5 billion committed equity facility, an uncommon pairing at the point of listing. The structure also includes a partnership with Animoca Brands to support stablecoin development, as well as plans to acquire a British Virgin Islands banking license, subject to regulatory approval. Its portfolio spans a range of real-world assets, including land and mineral resources, and is built around an AI-native banking model with stablecoin denomination.

Within this structure, NewCampus is engaged as an independent contractor providing what it describes as operational infrastructure. Rather than functioning as a traditional vendor, NewCampus is positioned as a longer-term partner, contributing to how the organization is set up to operate as it develops. The collaboration reflects a broader approach to building new financial institutions—one that pairs capital and technology with operational systems designed to support scale from the outset.

Beyond vendor relationships: The ecosystem builder approach

Most financial institutions operate through a network of vendors such as technology providers, and service firms, with each being responsible for a defined function. In that model, tools are delivered, frameworks are recommended, and implementation is often left to the organization itself. 

NewCampus’ role differs in scope to traditional models. Rather than delivering short term advisory and execution, NewCampus partners with organizations at the operational level, focusing on how people, systems, and processes are designed to function as institutions scale.

One area of focus is community infrastructure. For financial institutions—particularly challenger banks serving underserved or cross-border markets—growth depends on trust and sustained participation, not just customer acquisition. NewCampus designs systems that support long-term engagement, peer networks, and leadership pathways, 

For newer or challenger banks, trust and participation cannot be assumed. It needs to be deliberately designed. NewCampus brings experience in building community infrastructure that encourages long-term engagement, supports peer networks, and moves relationships beyond purely transactional interactions. This approach treats users as participants in a broader ecosystem—an important distinction for financial institutions operating across markets where trust, inclusion, and sustained engagement are critical.

A second focus is operational systems for innovation. As institutions combine traditional financial services with newer technologies such as blockchain and tokenization, NewCampus helps design internal processes that allow legacy systems and new infrastructure to coexist. The emphasis is on enabling teams to move quickly while remaining within regulatory constraints.

With experience operating across Asia and multiple regulatory environments, the company brings a cross-border perspective to expansion, shaped by building distributed communities and organizations. The underlying insight is straightforward: the skills required to build and scale a learning ecosystem—coordination, trust and adaptability—translate directly to building a financial ecosystem.

Also read: Southeast Asia’s marketing renaissance: How up-and-coming marketers are leading the charge

Building for what comes next: what this partnership signals for fintech and enterprise partners

The NewCampus–Soul World Bank collaboration reflects a broader convergence between sectors that were once treated as separate. 

For fintech partners, it points to collaboration models that extend beyond pure SaaS offerings, where technology is paired with partners focused on operations, governance, and community design. 

For enterprise solution providers, it highlights an opportunity to move upstream—from selling tools to helping shape how institutions are structured and operated. Soul World Bank’s partnership with Chainstarters, an AI and real-world asset firm based in Connecticut, reinforces this trend toward complex ventures being built through multiple specialized partners working in concert.

Looking ahead, the Soul World Bank transaction is expected to close in the first quarter of 2026, with all milestones subject to regulatory approval. Beyond the timeline, the partnership raises a larger question about the types of partners new economy institutions will require as they take shape.

NewCampus’ positioning reflects a track record of building operational infrastructure for complex ventures, first in education and now applied to finance, offering a collaboration model that may become increasingly relevant at the intersection of finance, technology, and community. Overall, NewCampus is expected to navigate cross border complexity with the same defensible formula: community creates the system that scales. 

The transferable skill: Building ecosystems that last

The same skills that enabled NewCampus to build a challenger university are now being applied in a new context: the formation of a challenger bank. At its core, ecosystem-building is a transferable competency—one rooted in designing systems where participation is intentional, trust can form, and operations are able to scale without breaking.

As finance continues to evolve, the partnerships that shape the next generation of institutions may be defined less by technology alone and more by the ability to build durable communities and operational infrastructure. In that sense, the NewCampus–Soul World Bank collaboration offers a glimpse into how complex, multi-stakeholder ventures may increasingly be built—through collaboration, specialization, and shared institutional design.

Technology partners interested in exploring operational infrastructure collaborations for new economy ventures can reach out to NewCampus to discuss potential partnerships.

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The e27 team produced this article sponsored by NewCampus

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The question isn’t whether your board understands today’s risks — it’s whether you’re governing for a future that hasn’t arrived yet.

As board members and executives navigating Asia’s dynamic markets, we face a distinct challenge: the very velocity of change in our region makes historical precedent an increasingly unreliable guide. What worked in governance terms even three years ago may be inadequate for the complexities emerging now. The regulatory shifts in China, the technological leapfrogging across Southeast Asia, the geopolitical realignments reshaping supply chains — these aren’t incremental changes requiring incremental responses. They demand boards that can anticipate, not just react.

Why futurist thinking belongs in the boardroom

Traditional governance frameworks ask boards to exercise oversight, ensure compliance, and manage known risks. This remains necessary but insufficient. Forward-looking governance recognises that the board’s fiduciary duty extends beyond protecting today’s enterprise value to stewarding the organisation’s relevance and resilience across multiple possible futures.

Consider the practical implications. When your board reviews a five-year strategic plan, are you stress-testing it against scenarios where digital currencies reshape treasury management, where carbon border adjustments fundamentally alter your cost structure, or where AI transforms not just operations but the very nature of competitive advantage in your sector? If these conversations feel speculative rather than essential, that’s precisely the gap forward-looking governance must close.

Also Read: How biotech is changing the global agriculture game for investors

The Asian context demands it

Our region presents specific imperatives. Family-controlled enterprises navigating generational transitions must balance legacy preservation with radical adaptation. State-linked entities face the complexity of commercial imperatives intersecting with policy objectives that themselves are evolving. High-growth companies in technology and manufacturing confront the reality that regulatory frameworks are being written in real-time, often in response to the very innovations they’re pursuing.

Moreover, stakeholder expectations in Asia are shifting with particular intensity. ESG is no longer a Western import but increasingly embedded in local capital allocation decisions, talent acquisition, and social license to operate. Boards that treat this as a compliance exercise rather than a strategic and operational imperative are already behind.

What forward-looking governance requires

This isn’t about crystal balls or abandoning governance fundamentals. It’s about augmenting traditional board competencies with three capabilities:

• Structured foresight: Building systematic processes to identify emerging risks and opportunities beyond the typical planning horizon. This means engaging with weak signals — the regulatory proposal still in consultation, the technology still in labs, the social trend visible in adjacent markets — before they become urgent crises or missed opportunities.
• Adaptive oversight mechanisms: Ensuring your governance architecture itself can evolve. When disruption accelerates, the cadence of board meetings, the composition of committees, and the information flows that boards rely upon may all need reassessment. Does your board’s calendar reflect the actual velocity of change in your business environment?
• Strategic courage informed by rigorous analysis: Perhaps most critically, forward-looking governance means cultivating the board’s capacity to make decisions under deep uncertainty. This requires both intellectual rigour — scenario planning, red-teaming assumptions, diverse expert input — and the institutional courage to act on convictions about the future even when consensus is elusive.

Also Read: Funding for good: Why investors should bet on tech with measurable social impact

An invitation to dialogue

Having worked across financial services, airlines, e-commerce, government, telcos, and more — from agile startups to sprawling, highly matrixed multinationals spanning Asian and global markets, I’ve watched boards grapple with these questions. The best ones aren’t just asking these questions. They’re embedding foresight into strategy reviews, bringing future-relevant expertise into the boardroom, and carving out real time to debate where they’ll be in five years, not just next quarter.

The boards that will distinguish themselves in the coming decade won’t be those that governed the past most efficiently, but those that prepared their organisations most effectively for futures that few could clearly see. This is the essence of forward-looking governance — and it’s not optional in Asia’s rapidly evolving landscape.

The question for your board: When you look at your agenda for the next meeting, what percentage addresses what has already happened versus what should happen next? The answer to that question may reveal how ready you are for the governance challenges ahead.

This article was first published on The Boardroom Edge.

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Southeast Asia’s startup ecosystem has entered a more sober phase. Capital is harder to access, growth expectations are sharper, and teams are being asked to deliver more with fewer resources. In this environment, hiring has quietly become one of the most critical and expensive decisions a company makes.

Yet, recruitment methods across the region have barely evolved. Many organisations still rely on manual resume screening, subjective interviews, and long coordination cycles. These approaches may have worked when teams were small and timelines forgiving, but they struggle when companies hire across countries, functions, and time zones. This growing mismatch between how companies hire and how fast they need to operate is where AI is beginning to play a meaningful role.

The real cost of slow and inconsistent hiring

In a tighter market, hiring mistakes show up quickly. A delayed hire slows execution. A poor hire drains management time and morale. For early- and growth-stage startups, these costs compound fast. Across Southeast Asia, several issues are common:

• Recruiters are overwhelmed by application volume
• Interview quality varies from one interviewer to the next
• Scheduling stretches hiring cycles unnecessarily
• Early-stage bias filters out capable candidates
• Candidates disengage due to slow or unclear processes

These issues directly affect a company’s ability to execute, particularly when operating with lean teams and limited runway.

Why precision hiring matters more than ever

In today’s market, hiring is no longer just about filling roles quickly. It is about making fewer mistakes and getting more value out of every hire. This is where precision hiring becomes critical.

Precision hiring means reducing guesswork at every stage of the recruitment process and clearly defining what a role actually requires, evaluating candidates against consistent criteria, and making decisions based on evidence rather than intuition alone. As startups operate with tighter budgets and leaner teams, the margin for hiring error has narrowed significantly.

In Southeast Asia, this need is amplified. Talent markets are diverse, career paths are often non-linear, and resumes do not always reflect true capability. Relying solely on unstructured human judgment increases the risk of bias, inconsistency, and missed potential. Two interviewers can walk away from the same conversation with very different conclusions. Multiply this across teams and countries, and hiring outcomes become unpredictable. As organisations scale, this inconsistency turns into a real operational risk.

Also Read: The future of recruitment in Web3 era

AI enables precision by introducing structure where human effort struggles to scale. It helps clarify job requirements, standardise early evaluations, and surface clearer signals about candidate capability. The result is not automated decision-making, but better-informed human judgment.

A shift toward structure and skills

Many startups are rethinking how they evaluate talent, and three shifts stand out.

First, there is a move toward skills-based hiring. Capability is increasingly valued over pedigree, which better reflects how talent develops in emerging markets.

Second, companies are recognising the need for standardisation. As teams grow, hiring can no longer depend solely on individual interview styles. Shared evaluation criteria are becoming essential to ensure consistency.

Third, AI is being introduced in areas where human effort does not scale well—particularly in screening and early-stage interviews.

Where AI actually helps

The most practical use of AI in recruitment today is not decision-making, but consistency. AI-led or AI-assisted interviews help standardise early-stage conversations. Questions are structured, follow-ups are consistent, and candidates are assessed against the same dimensions.

For startups, the impact is tangible. Hiring cycles shorten. Candidate drop-off reduces. Feedback becomes more reliable. Recruiters spend less time coordinating and more time evaluating. AI manages volume; humans retain judgment.

AI also addresses long-standing challenges such as high application volumes, subjective interviews, slow scheduling, delayed feedback, and unconscious bias—issues that have historically weakened decision-making and damaged candidate experience.

From gut feel to clearer signals

Hiring will always involve intuition, but intuition works best when supported by clear signals. AI tools increasingly provide structured input such as interview transcripts, skill alignment, communication clarity, and problem-solving indicators.

These insights do not replace human judgment. Instead, they make it more grounded. Some platforms apply this model by structuring interviews and evaluations while leaving final decisions with hiring managers. When used thoughtfully, this approach improves consistency without removing human context.

Also Read: AI-powered recruitment: Revolutionising hiring in Southeast Asia

AI-enabled recruitment systems also help standardise job requirements, accelerate resume screening, automate scheduling, and capture feedback in a comparable, data-backed format. Together, these capabilities enable faster hiring cycles, fairer evaluations, and smarter decisions—without proportionally increasing recruiter workload.

What this means for startups

As Southeast Asia’s startup ecosystem matures, execution quality will matter more than speed alone. Talent decisions sit at the heart of execution.

Over the next decade, hiring is likely to become faster but more deliberate; more structured yet still human-led; focused on capability rather than credentials; and increasingly transparent and accountable. Startups that adapt their hiring practices early will make fewer costly mistakes as they scale.

AI is not here to replace recruiters or founders. Culture, leadership potential, and team dynamics cannot be automated. What AI can do is remove friction — long delays, inconsistent screening, and avoidable bias, so humans can focus on decisions that truly require judgment.

In today’s startup environment, hiring is a strategic function. AI is not changing hiring by making it impersonal; it is changing hiring by making it more precise. For startups in Southeast Asia, precision hiring may prove to be one of the most important advantages they build in the years ahead.

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Singapore’s modern workforce is an intricate tapestry woven not only from diverse cultures and skills but also from distinct generational experiences and expectations. Today, the office is commonly composed of Gen X, Millennials, and Gen Z, each shaped by unique social contexts and world events that influence their personalities, working attitudes, and career goals.

As of 2024, Millennials make up the largest share of the workforce at 43 per cent, followed by Gen X at 32 per cent, and Gen Z at 15 per cent. While Gen Z currently represents a smaller proportion, their presence is expected to grow rapidly. Projections suggest they will make up around 25 per cent of the workforce by 2030, largely shaping the trajectories of the employment landscape.

Gen X professionals have matured during turbulent times such as the Asian Financial Crisis, the Cold War, and the dawn of the internet era. They are often known for their resilience and work ethic. The economic uncertainty and rapid technological advancements they faced influenced their reputation as steadfast grinders, keen on climbing the professional ladder.

Millennials, known as the “sandwich generation,” bridge Gen X’s steadfast grinders and Gen Z’s vocalists. Launching careers amid globalisation and digital growth, they adapted quickly to evolving technologies and became agile hustlers. While tech-savvy, their skills are often surpassed by Gen Z’s native fluency, and they have adjusted their work styles as Gen Z shifted workplace culture toward new values and priorities.

Gen Z are true digital natives, shaped profoundly by the acceleration of Gen AI technologies such as ChatGPT. This generation champions flexibility, balance, mental health, and purposeful work, marking a clear contrast to older cohorts. Distinctively, Gen Z are vocalists in the workplace— unafraid to speak their minds.

The blend of these generations brings both vibrancy and complexity to Singapore’s workplaces. Employers face challenges in harmonising diverse mindsets, skillsets, and expectations across age groups. An employer may themselves embody a different generational perspective than their team, making “one size fits all” management strategies ineffective. Understanding these nuanced differences is essential to building inclusive, resilient, and innovative workplaces that leverage generational strengths.

Generational differences in career aspirations

Generational differences in career priorities are evident across Singapore’s workplaces. It shapes not just what individuals value, but also how employers must engage and retain talent.

For Gen X, the digital and automation era has intensified concerns about job stability and security. This generation remains attentive to practical needs—competitive compensation, healthcare benefits, retirement savings (such as CPF), and supporting children’s education—reflecting a focus on security and tangible rewards as they navigate the risk of technological displacement.

Millennials, by contrast, are driven by aspirations for career progression and development opportunities. They seek clear advancement pathways, leadership roles, and continuous learning, wanting to work for organisations that offer purposeful missions and tangible social impacts alongside professional growth. For these workers, personal fulfilment and societal contribution increasingly intersect with traditional ambitions.

Gen Z, meanwhile, diverge even further—valuing flexibility and autonomy above all. For them, hybrid work options, flexible hours, and freedom in how tasks are approached are not added perks, but basic expectations in the modern job landscape. Just as importantly, Gen Z highly prioritises work-life balance and the ability to pursue interests beyond work, placing strong emphasis on mental health and personal well-being. They expect employers to support this ethos, making the pursuit of balance and autonomy integral to their choice of workplace.

Also Read: Are you a human resource?

Rising costs of living and salary transparency have driven Gen Z fresh graduates to enter the workforce with significantly higher salary expectations compared to previous generations. According to the 2024 Graduate Employment Survey, the median gross monthly salary for fresh graduates in full-time permanent employment rose to SG$4,500 (US$3,492), up from SG$4,317 (US$3,350) in 2023. This shift reflects heightened salary demands by the younger cohort, leading some employers to hesitate in hiring fresh graduates. Some opt for candidates with industry experience or replace roles with technology due to cost considerations.

Given such diverse priorities and evolving salary expectations, employers can no longer rely on traditional offerings like salary, annual leave, or medical benefits alone to attract, motivate, and retain talent. Instead, organisations must adopt a more holistic, flexible approach—empowering line managers to work closely with team members.

Embracing digital diversity for workplace cohesion  

Technological disparity among Gen X, Millennials, and Gen Z is a defining feature of today’s multigenerational workplace, requiring thoughtful attention from employers before introducing new processes or systems.

Gen X entered the workforce amid typewriters, fax machines, and the earliest computers. For many, digital adoption occurred mid-career, where they picked up productivity tools like Word, Excel, and email. However, they may be less comfortable with advanced cloud collaboration, data analytics tools, or AI-driven software unless they have upskilled through training. Their strengths often lie in institutional knowledge and business acumen rather than digital agility, making rapid adoption of new tech platforms a greater challenge.

Millennials, whose formative years coincided with the rise of Windows computers, Internet connectivity, and mobile phones, have a natural ease with digital tools and communications. Most are proficient in enterprise platforms, social media, and online research, adept at adopting new digital workflows, and flexible with evolving work technologies. However, they may still feel less “native” than Gen Z when it comes to cutting-edge trends like AI prompt engineering, advanced data visualisation, or blockchain solutions.

Gen Z, on the other hand, are truly digital natives—raised in an environment dominated by smartphones, high-speed internet, and cloud-based platforms. Their exposure to coding, digital creation tools, and seamless multitasking across devices means they possess unparalleled digital agility and confidence in picking up new apps or software. They are quick to adopt new tools but may lack depth in legacy enterprise systems and soft skills needed.

Also Read: Anchanto CEO on why human resource is essential for a growth stage startup

For employers, the difference in the pace of technology adoption across generations cannot be overlooked. Gen X may show resistance when new systems are introduced, requiring more support and reassurance. Educating older workers on the use and benefits of technology is beneficial, giving them time to adapt and creating opportunities to build new capabilities, such as AI adoption. By recognising these varying paces and adopting inclusive strategies, organisations can harness the strengths of all generational cohorts and achieve cohesive progress in an increasingly digital business environment.

Navigating generational communication styles at work  

Different generations in the workplace exhibit distinct communication styles shaped by their formative experiences and technology exposure.

Gen X professionals, accustomed to traditional modes of communication and with a preference for direct, concise communication, typically prefer face-to-face interactions and formal channels like email. For most of their careers, remote work was uncommon, and many relied on direct, official communication methods for clarity and efficiency.

Millennials began their careers similarly but adapted to more digital communication tools with the rise of remote work, especially during the COVID-19 pandemic. They value frequent feedback and are versatile, comfortably switching between emails, instant messaging, and video conferencing based on the context, showing adaptability in communication preferences.

Gen Z entered the workforce post-pandemic, with remote and hybrid work norms firmly established. They prefer informal, casual, quick communication through platforms like WhatsApp, Microsoft Teams, or Zoom.

Also Read: Scaling is hard: Here are 7 things Human Resources can do to manage it

While many Gen Zs are vocal about workplace matters, this tends to be the case only when they feel engaged — disengaged individuals are often less outspoken. Employers should actively demonstrate that feedback is heard and acted upon to better manage and retain this cohort.

With these generational differences, communication gaps can arise if no understanding is established, potentially leading to miscommunication. Employers must foster awareness and create environments where diverse communication preferences are respected and bridged effectively, ensuring that message delivery remains consistent and inclusive across all generations.

Leading across generations: A call for flexibility and inclusion

In today’s diverse workforce, differences exist not only among employees but also within leadership and management teams, as individuals come from varying generations. These generational differences can significantly impact working relationships, team dynamics, and overall performance.

Employers must recognise that a one-size-fits-all approach will not work. As Gen X and Millennials increasingly find themselves managing Gen Z, it is vital to recognise that this younger cohort brings a distinct set of expectations and perspectives on work. Leaders must step beyond their comfort zones to communicate, engage, and include Gen Z in meaningful ways. When properly engaged and their energy channelled, Gen Z can be a powerful asset. They can leverage their digital agility to challenge the status quo, driving innovation and strengthening the business landscape.

Acknowledgement: Bahvaani A, Assistant Research Manager, IndSights Research.

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I have always believed that a voice does not expire. It grows, shifts and sometimes hides, but it never disappears.

As a coach, I encourage people to share their stories. On stage. Online. In classrooms and in life. Some tell their stories with trembling voices. Some cannot speak them at all.

Now with AI, a new kind of storytelling is possible.

When the story is true, it finds a way

I often tell my learners, AI can help you tell your story, but it cannot feel it for you. You can use AI to help with your script, your visuals or even your voiceover. But the story still needs to come from your truth, your thoughts, your emotions, your experiences.

AI can generate words, but it is your meaning that gives them heartbeat. AI can sing your song, but it is your story that gives it soul.

When I created my first AI song, I put pieces of my own story into it. It was fun, simple and a little silly, but it felt alive. Because even when a digital voice sang it, the story behind it was mine. Our voices evolve, they do not end.

From stage to screen to AI storytelling

Not everyone dares to speak on stage. Some struggle to record videos because they do not like their voice or fear judgment.

Now with AI narration, you can still tell your stories. You can write your thoughts and let AI speak them for you. You can create a video, an animation or even a song that carries your message.

AI gives us another way to share what matters. It does not replace us. It supports us. It is like having a digital friend whispering, you can do this.

The important thing is not how you tell the story. It is what you do.

Also Read: AI, authenticity and the future of founder storytelling

Stories that evolve, not fade

The most beautiful part of AI storytelling is that it preserves stories that might otherwise be lost. You can record your wisdom, your memories, your ideas and let them travel further than your lifetime. You can turn reflections into AI videos, podcasts or songs. You can even animate your story so your grandchildren will one day see and understand who you were.

That is legacy, not fame, but remembrance.

Our stories evolve. From notebooks to microphones. From live stages to digital screens. And now, through AI voices that echo our own truths.

The creative inside me

The creative inside me is enjoying every new AI feature and trying everything I can. Calling all the crazy, creative people out there, get those juices spilling out. No right or wrong. Art is a rebel. So create. Experiment. Play.

Let the tools help you discover new ways to express who you are because creation is not about control. It is about freedom.

The gentle reminder

You do not need to sound perfect to be powerful. You just need to be honest.

AI can help shape your story, sing it or even speak it for you. But what matters most is that it is still your story. Your thoughts. Your ideas. Your voice.

So whether you share it on stage, online or through AI storytelling, remember this. Your voice does not expire. It evolves.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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Image generated using AI.

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