Grab Holdings has formally become the majority shareholder of Superbank, marking a strategic escalation in the Singapore-headquartered group’s push to control financial services infrastructure in Southeast Asia’s largest economy.

The ownership milestone was reached after related entities, including A5-DB Holdings and GXS, acquired additional shares in May 2026, pushing Grab’s effective stake above the controlling 50 per cent threshold.

Also Read: Superbank, Genesis launch US$40M financing solution for Indonesian startups

The move cements Grab’s role not merely as a distribution partner but as a controlling owner of a licensed bank operating in Indonesia, where mobile-first financial services are rapidly reshaping consumer behaviour.

A clearer route to scale lending

Superbank has been one of the most prominent success stories among Indonesia’s digital lenders. The bank reported a 55 per cent year-on-year increase in its loan portfolio as of April 2026, a surge industry observers attribute largely to tighter integration with the Grab and OVO consumer ecosystems. Those platforms offer abundant data and customer touchpoints, from ride-hailing and e-commerce to payments, that can be used to underwrite loans and cross-sell financial products.

Profitability has followed growth: Superbank’s profit before tax leapt 1,529 per cent to 142 billion rupiah (about US$7.81 million) for the four months ending 30 April 2026. While the absolute profit figure remains modest relative to legacy banks, the scale of the improvement signals that digital distribution and low-cost customer acquisition can rapidly compress time-to-profitability when a bank is embedded within a large consumer platform.

This commercial logic appears central to Grab’s willingness to convert a commercial partnership into outright control. Owning a bank removes certain regulatory frictions around product development and gives Grab greater latitude to integrate credit, deposit and payment services across its apps.

Consortium backing and strategic partners

Superbank’s ownership reflects a consortium approach that mixes regional tech companies with local media and telco know-how. Alongside Grab, Singtel, KakaoBank, and Indonesia’s Emtek Group, these backers have steered product development and distribution since the bank rebranded from Bank Fama International to Superbank in 2023.

Also Read: Digital banks win transactions, not loyalty: A missed opportunity in Indonesia

That consortium model has been influential in Superbank’s rapid product rollout. Singtel and KakaoBank bring regional digital-banking experience, while Emtek offers local distribution channels and brand recognition. For Grab, the arrangement combines foreign capital and regional expertise with on-the-ground local partners, a pragmatic route into a market where domestic understanding and regulatory navigation remain crucial.

A crowded, competitive market

Grab’s majority stake comes at a moment when Indonesia’s digital banking sector is noticeably crowded. Regulators have licensed some 17 digital banks, and policy changes have encouraged foreign participation, allowing non-Indonesian investors to own up to 99 per cent of local lenders. That regulatory openness has invited cross-border competition, with established internet giants, telcos and financial groups all vying for scale.

For Grab, securing majority control of Superbank is both an offensive and defensive play. Offensively, it positions the company to accelerate product innovation, from point-of-sale financing to savings and insurance, while using its consumer touch points to drive scale. Defensively, it pre-empts rivals from buying the same infrastructure or forming competing alliances that could lock Grab out of lucrative financial flows generated by its apps.

The Southeast Asian angle

Indonesia is a bellwether for digital finance across Southeast Asia. With hundreds of millions of mobile-first consumers, many still underbanked or underserved by traditional lenders, the opportunity for platform-led banks remains substantial. Grab’s acquisition therefore has implications beyond Indonesia — it signals an intensifying phase of consolidation in Southeast Asia, where platform companies are moving from partnerships to ownership of financial infrastructure.

Other markets in the region will watch closely. If Superbank’s model — rapid user acquisition via platform integration, machine-learning-based credit underwriting, and low marginal cost distribution — continues to deliver outsized growth and profits, it could accelerate similar moves elsewhere. Regulators in countries such as the Philippines, Vietnam and Thailand are also revising digital banking rules, and Grab’s latest step will likely shape competitor strategies and regulatory conversations across the region.

Questions and risks

Despite the strategic logic, owning and operating a bank brings new sets of risks. Credit quality can deteriorate rapidly if underwriting standards loosen during aggressive origination pushes, competition could compress interest margins, and regulators may tighten oversight as digital banks grow systemic importance. Grab will need to demonstrate robust risk management, capital adequacy and operational resilience as Superbank scales.

Also Read: How digital banking is driving financial inclusion in SEA

There is also the broader question of ecosystem concentration. Critics argue that platform companies owning banking infrastructure can create single points of control over many aspects of consumers’ economic lives. Regulators balancing financial inclusion goals against concentration risks may respond with stricter scrutiny, a dynamic that could complicate rapid expansion plans.

What’s next

For now, the acquisition gives Grab a stronger hand in shaping the future of embedded finance in Indonesia. The company can expand credit and savings product distribution through its app, OVO, and partner networks, while experimenting with product bundles that tie payments, lending and marketplace services together.

How successfully Grab translates control into sustained, responsible growth at Superbank will influence whether the move becomes a template for further consolidation across Southeast Asia, or a cautionary tale of the challenges that come with running a bank in one of the world’s most dynamic digital-finance markets.

The post Superbank under Grab: what the takeover means for Indonesia’s crowded digital banking scene appeared first on e27.

Modern entrepreneurship dictates that we must be software-first, where there is low capital expenditure, rapid prototyping, and infinite scalability. This approach is designed to attract venture capital, which views hardware, inventory, and physical assets as toxic liabilities that inhibit explosive growth.

The result is a market saturated with businesses that are US$50,000 to start and US$50 million to sell, built on the fragile foundation of easily replicable code.

The contrarian truth, which I have seen validated across multiple industries, is this: High initial capital expenditure is a strategic advantage. Founders who prioritise an asset-first approach (embracing complexity, physical friction, and substantial up-front investment) are building businesses with superior long-term moats, stronger unit economics, and vastly higher liquidation value.

The liability of the easy start

The bad of the Software-First approach is the liability it invites. When the barrier to entry is low, competition is instantaneous and fierce. A successful SaaS application, having raised US$2 million, must spend the next five years fighting off dozens of highly efficient, low-cost competitors that can replicate the code and the business model in under a year. The capital is spent on fighting for market share, not on product differentiation.

Conversely, consider the asset-first approach:

A founder decides to enter the specialised commercial equipment rental market, focusing on niche, highly regulated machinery (e.g., cryogenic freezers for bio-labs or specialised aerial drones for industrial inspections).

• The bad (initially): They must secure US$2 million in debt or equity immediately to purchase the equipment. The process is slow, involves negotiation, legal work, and insurance. The market says this is inefficient.
• The good (long-term): The US$2 million in expenditure is now a non-replicable barrier to entry for every competitor.

The competitor cannot start their business tomorrow with a credit card and a laptop; they must raise the same US$2 million, navigate the same procurement hurdles, and wait the same six months for delivery. This friction creates a long-term moat that code simply cannot replicate.

Also Read: Funded: The quieter capital path founders keep missing

The unit economics advantage

The asset-first model, while demanding initial capital, offers significantly better control over long-term unit economics.

In a pure software business, the gross margin is high (often 80 per cent), but the customer acquisition cost (CAC) and customer retention costs are perpetually volatile and must be paid monthly. Competitors can always bid up ad costs or undercut subscription fees, eroding that high margin.

In the asset-first model, once the initial capital is spent, the business controls a tangible, revenue-generating asset.

• Fixed cost stability: The cost of the asset is fixed. The monthly revenue (rent, processing fee, etc.) is directly tied to a physical object, allowing for highly stable, predictable cash flow that is protected from digital price wars.
• Liquidation protection: If the business fails, the founder retains the core asset (the equipment, the real estate, or the specialised inventory), which retains tangible liquidation value. A failed software startup leaves behind a pile of worthless code and depleted cash. A failed specialised equipment rental company retains the equipment, which can be sold to recover the initial investment.

The future: The asset-first premium

The future of durable business formation will see a strategic pivot away from the pure-software model toward asset-first businesses leveraging digital tools.

Also Read: Burning billions: AI’s capital frenzy and its global implications

The smartest founders are not avoiding assets; they are seeking out industries where the initial capital outlay is necessary to create a structural, long-term choke point. They are building businesses that are intrinsically connected to the physical world, using technology only to optimise the deployment of that asset, not to be the core product.

This involves:

• Choosing complexity: Deliberately selecting regulated niches (waste processing, specialised healthcare logistics, commercial agriculture) where high start-up costs repel the vast majority of founders and VCs looking for quick flips.
• Capital as a weapon: Viewing every large capital expenditure not as a liability, but as a defensive barrier erected against future competition.
• Prioritising downside protection: Structuring the business so that failure still returns a significant portion of the initial investment, a luxury pure-software founders rarely enjoy.

We must stop worshipping the ease of the lean start and recognise that true, enduring success often requires embracing the complexity and high cost that creates a definitive, structural moat. The US$5 million factory may have been hard to build, but it was a better investment than the US$500,000 code repository that can be cloned and undercut by the next team of efficient developers.

Are you building a business that requires a competitor to raise 10 times your initial capital to compete, or are you building a business that can be started with a credit card and a weekend of coding? Is your priority low initial cost, or long-term, non-replicable profitability?

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Most conversations I have with other founders about AI land quickly on the same question: what can we automate? It is a reasonable instinct. But for founders and leadership teams building AI-native businesses, the more consequential question is a different one: if AI handles more of the execution, what exactly are humans for?

The answer is more specific than “creativity” or “vision,” two words invoked so often they have lost their precision. The human skills that matter most in an AI-first business are strategic thinking and systems thinking. Together, they form the architecture layer that determines whether an AI-native business compounds in value, or simply runs faster toward the wrong outcomes.

Why these two skills, specifically

There is a structural reason why these skills remain irreplaceable, beyond familiar arguments about nuance and context.

AI is backwards-looking by design. Every model, every output, every recommendation is built on patterns extracted from past data. You can feed it new context and real-time signals, and it will process them intelligently. But its underlying reasoning is always a function of what has already happened.

Strategic thinking is fundamentally forward-looking. It requires forming a perspective on where a market is going before the evidence is conclusive, on what customers will want before they can articulate it, on which bets to make when the data is incomplete by definition. The best strategic decisions are made precisely in the space where historical patterns are the least reliable guide. That is the space AI cannot occupy.

Systems thinking adds a different dimension: the ability to see how parts of a business interact, how a change in one area creates second and third-order effects elsewhere, and how the overall system produces outcomes individual components cannot explain on their own. Without someone thinking at the system level, AI initiatives tend to solve isolated problems while creating new friction at the handoffs between them.

Both skills are intuition-heavy, built on real-world experience, judgment grounded in a specific business context, and a tolerance for ambiguity that cannot be trained into a model on historical data.

Also Read: What to actually prioritise when your board wants AI and everything feels urgent

How to choose

The practical question for any leadership team is what goes to AI, what stays with humans, and what requires both. A simple Signal vs. Execution layer model structures every capability across three levels.

• Execution is where defined tasks get done: content generation, data retrieval, report formatting, customer query handling, workflow automation. AI operates excellently here. The inputs are bounded, the success criteria are measurable, and the patterns are well-established. It is now reasonable to default to AI at this layer and free your team from the cognitive overhead it was consuming.
• Orchestration is where workflows are coordinated across functions, systems, and agents. AI can support this layer, but someone has to have defined the logic, the rules, and the exception-handling criteria that make coordination work. Systems thinking is what makes orchestration coherent. A human needs to own this layer even as AI tools increasingly execute within it.
• Direction is where the business decides what it is building, why, and in what sequence. AI can inform direction with data and scenario analysis. It cannot own it. Direction requires the forward-looking intuition that AI structurally lacks, and the systems-level awareness to understand how today’s choices shape tomorrow’s constraints.

A practical example: we recently rebuilt the GTM stack for one of our SaaS businesses to be fully AI-native at the Execution and (partially) Orchestration layers. Prospecting, lead qualification, outreach sequencing, and follow-up logic all run through AI-driven workflows. The team now spends 30 to 45 minutes per week on that function instead of roughly 20 hours, with a 1.8x higher response rate and 1.6x higher close rate. The Direction layer did not change. Who to target, what positioning to lead with, and which segments to prioritise remained entirely human. What changed is that AI executes that judgment at a scale and consistency no manual process could match.

The errors I see most businesses make are automating Direction (outsourcing strategic and systems choices to tools that optimise for past patterns) or leaving Execution to humans (wasting human capacity on tasks AI handles better). The framework is a forcing function to avoid both.

Also Read: Think with AI: The new skill for social entrepreneurs

What to pay attention to in the next 12 months

Competitive pressure will push many teams to deploy AI broadly and quickly. Some of that pressure is real. A lot of it will produce systems that move fast in the wrong directions.

Three things worth keeping close.

• Audit your direction layer: How many strategic choices are currently being made by default, through inertia, or by deferring to tools and benchmarks? If your team spends most of its cognitive energy at the Execution layer, Direction is likely underinvested.
• Build systems thinking as an organisational capability: The businesses that compound from AI are those where someone is consistently asking: how does this initiative interact with everything else we are building? This thinking needs to be embedded in how you design and iterate on AI deployments, not just exercised at the top.
• Resist the pull toward AI as a strategy: Using AI is not a strategy. It is a capability. Durable competitive advantage comes from a clear view of where you are going and a coherent system for getting there, with AI as a powerful layer within that system.

The point

AI will keep getting better at execution, and it will increasingly support orchestration. The layer it cannot occupy is Direction, and the reason is structural: it is built on the past, and Direction requires a view on the future.

Strategic thinking and systems thinking are what make the difference between an AI-native business that compounds and one that scales its existing assumptions faster.

These are the skills worth protecting, developing, and keeping close to the center of how you make decisions.

—

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

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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In modern meeting rooms today, there is a scene that feels both strange and normal at the same time. Someone opens a laptop, types a few sentences, and within seconds, a business proposal appears, along with market analysis, source code, presentation designs, and even marketing strategies that once required a small team working for days. On the other side of the room, someone sits quietly for a moment before speaking. They are not slower. They are thinking: “Is this the right decision for the people who will be affected by it?”

Moments like that are beginning to change how we understand human value.

For decades, humans were valued for their ability to process information. The faster someone could calculate, memorise, write reports, or organise data, the more valuable they became inside organisations. But AI is beginning to shift that foundation. Machines can now write faster, analyse more broadly, read thousands of documents without fatigue, and even generate ideas that appear creative. Many professions are starting to experience a quiet discomfort: if AI can perform most intellectual tasks, then where exactly does human value still exist?

That question is no longer philosophical. It is becoming personal.

A programmer watches AI generate hundreds of lines of code in seconds. A designer sees AI create illustrations from a single prompt. Analysts watch dashboards and insights appear automatically. Even writers quietly wonder whether readers can still distinguish between human writing and machine-generated text.

Yet the longer we live alongside AI, the more something interesting becomes visible: speed was never the true core of human value.

AI is incredibly powerful at answering. But humans still live in a much greyer territory: deciding which questions are worth asking.

And that difference matters.

AI can help companies optimise profits. But humans decide whether those profits are achieved in ways that damage or strengthen society. AI can help underwriting systems assess risk within seconds. But humans understand what it feels like to be afraid because of illness, unemployment, or the desire to protect one’s family. AI can create highly efficient business strategies. But humans decide whether organisations still retain a sense of humanity.

That is where I have started to believe human value is not simply “the things AI cannot yet do.” That definition is too fragile. AI capabilities will continue to evolve. If human value is defined only by the remaining gaps in machine capability, then our identity will continue shrinking every year.

What feels more fundamental is this: humans give meaning to decisions.

Also Read: What to actually prioritise when your board wants AI and everything feels urgent

AI generates possibilities. Humans choose the consequences.

And choosing consequences means bringing morality, empathy, fear, hope, life experience, personal wounds, culture, and even love into the process of work. Those things are difficult to measure in spreadsheets. Yet they are often what separates systems that are merely efficient from systems worth preserving.

The problem is that the narrative around “uniquely human skills” sometimes sounds too comforting. Many conferences claim the future will belong to empathy, creativity, leadership, and communication. It sounds beautiful. But honestly, AI is already entering those spaces too. AI can now speak warmly, write poetry, act as a “companion,” and generate fairly creative ideas.

So are we truly redefining human value? Or are we simply rebranding the tasks that automation has not reached yet?

I do not think the answer is clear.

And perhaps that uncertainty is important to acknowledge.

Because there is a strong possibility that a large portion of human work will change dramatically. Not only repetitive jobs. Creative and strategic work is being reshaped too. Many people are quietly experiencing a professional identity crisis. They once felt valuable because of specific expertise. Now that expertise can be replicated by AI at low cost and extraordinary speed.

At that point, humans are forced to confront a question deeper than “How do I remain competitive?”

The question becomes: “Who am I when my abilities are no longer rare?”

And that is not a question AI can answer for us.

I see this shift directly in technology itself. In the past, engineers were valued primarily because they could write complex code. Today, AI can generate much of the boilerplate work. But the engineers who remain truly valuable are becoming those who understand business context, operational risk, stakeholder conflict, long-term implications, and decision-making under uncertainty.

In other words, human value is shifting from production toward judgment.

Also Read: Think with AI: The new skill for social entrepreneurs

It is no longer about who can build something the fastest. It is about who can wisely decide what should be built in the first place.

Strangely enough, those abilities are often born not only from formal education, but from life itself. From failure. From loss. From making bad decisions. From leading people during difficult situations. From understanding that behind every dashboard metric are real human lives.

Perhaps that is why future organisations will change how they hire people.

Not only by measuring technical skills, but by evaluating the ability to think across contexts. The ability to navigate ambiguity. The ability to maintain moral direction while systems become increasingly automated. The ability to build trust in a world flooded with synthetic content and algorithmic decisions.

Ironically, the more advanced AI becomes, the more expensive human trust becomes.

Because in an era where almost everything can be generated by machines, people begin searching for something that feels real.

Not merely correct according to data, but emotionally sincere.

Not merely efficient, but humane.

Not merely intelligent, but morally accountable.

At the same time, I do not want to romanticise humanity too much. Humans are also filled with bias, ego, manipulation, greed, and error. Many of the world’s worst decisions were made by humans, not AI. So not everything “human” is automatically good.

Which is why the future may not become a battle between humans and AI.

It may instead become an ongoing negotiation between machine capability and human wisdom.

And we still do not fully know what that final shape will look like.

Perhaps many jobs will disappear, while entirely new roles emerge that we cannot yet imagine. Perhaps organisations will become far smaller yet more productive because they are supported by AI agents. Perhaps one person will be able to build a global company with only a handful of people and a network of AI systems. Perhaps degrees, titles, and traditional corporate structures will slowly lose meaning.

Or perhaps humans will simply grow exhausted from living inside worlds that feel overly automated, and begin valuing slower, more authentic, imperfect human interaction again.

I do not know.

But one thing feels increasingly clear.

Also Read: How AI is changing what an SME team actually looks like

Human value may not come from being faster than AI.

Nor from being smarter than machines.

Perhaps human value emerges from our ability to retain consciousness, responsibility, and meaning in a world where almost everything can be automated.

And perhaps the most important question is not:

“Will AI replace humans?”

But rather:

“When almost everything can be done by machines, what are the things we still want to preserve as deeply human?”

—

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

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post In a world optimised by AI, what is left for humans? appeared first on e27.

Jiang Tao did not set out to build a music AI company. He set out to give his wife a gift. That detour, a decade in the making, has produced one of China’s most talked-about AI music platforms and a quietly ambitious global expansion play.

There is a moment in most founder origin stories where the mission and the person become indistinguishable. For Tao, founder of Shanghai and Beijing-based AI startup Initiai.on, that moment happened not in a boardroom or an accelerator cohort, but in a recording session with his daughter.

Also Read: How AI and Web3 are rewiring music’s infrastructure for a new creative economy

“I decided to train a model to generate a song for my wife as a gift,” Tao told e27 on the sidelines of BEYOND Expo 2026. “I used three years to train models that could generate melody and lyrics. And then my daughter and I sang the song together — the melody, the lyrics, all generated by the model.”

He pauses before adding, almost as a footnote: “I had never studied music before.”

What he did have was twenty years of machine learning experience, rooted in a PhD focused on speaker recognition, the science of identifying who someone is from their voice. That technical foundation, combined with a personal obsession he freely admits started as a romantic gesture, became the thesis behind Initiai.on: that AI could unlock musical self-expression for people who had never had access to it before.

From Tencent Music to founding his own model

Tao spent part of his pre-startup career at Tencent Music, one of the largest music streaming and entertainment platforms in the world, where he was part of an internal team exploring music generation. It was also around that time that he watched a pivotal case study unfold in the global AI music space.

“Have you heard of Suno, the most popular music generation company?” he asks. “At first, they did not want to generate music; they wanted to train a speech generation model. They uploaded a model named Bark to GitHub. People found that Bark could generate songs with vocals and background music. From that point, Suno turned to generate songs, not speech.”

The accidental discovery that audiences wanted AI-generated music, not just AI-generated speech, was a signal Tao took seriously. Combined with his own years of research and his Tencent experience, it gave him the conviction to go foundational: to build Initiai.on’s models from scratch rather than layer applications on top of existing APIs.

Also Read: How Wubble AI is transforming commercial music creation

“At that point, I knew I must do this full time,” he says. “It was so exciting.”

Yinchao: millions of users, and a grandfather singing to his grandchildren

Initiai.on’s flagship consumer product, Yinchao — a one-stop AI music creation and consumption platform built on the company’s self-developed large model — has crossed a user base in the millions. The platform also generated the official theme song for the 2025 World Artificial Intelligence Conference (WAIC), a signal of both the model’s technical maturity and its growing institutional credibility.

But the users Tao describes with the most evident pride are not the engineers or the institutional clients; they are the everyday people who had never thought of themselves as musicians.

“Even people without music knowledge can use this model to generate a song, to record their story,” he explains. “You record 30 seconds in our app, and we can embed your voice, and use that to generate the sounds. And many people on our app generate songs for their fathers, mothers, grandparents, grandchildren.” He smiles. “I like every day to read their stories, listen to their stories through their songs.”

The platform also serves professional musicians at the other end of the spectrum — artists with a hundred musical ideas and the budget to properly produce only a handful. Yinchao lets them generate full demos rapidly, stress-testing concepts before committing to full production.

On the devaluation question

The obvious provocation in any interview about AI music is the question of whether the technology devalues human creativity, commoditising the most intimate form of human expression. Tao does not dodge it, but he reframes it in a way that reflects both his engineering background and what sounds like genuine conviction.

“In China, people always pay for their emotions,” he says. “Some people don’t care whether the music was generated by a human or by technology. All they care is whether this music makes them happy? Does it make me want to cry? Does it satisfy my emotions?”

His second argument is economic access. Before tools like Yinchao, commissioning a custom song as a personal gift could cost upwards of US$2,000. “Only a few people could do this. Now everyone can.” He is not dismissive of human artistry, though. “I think genuine creative people can think of patterns that a model cannot generate. That is the most valuable thing in humanity.”

Going global, starting this week

For most of its existence, Initiai.on has operated primarily in the Chinese market. That is changing. At BEYOND Expo, Tao confirmed the company is launching a new product called Hitok, aimed at international markets including Australia, India, and Europe.

The monetisation model will differ by region. In Western markets, the platform operates on a credit-based system for generating music and music videos. In China, users pay for other formats of engagement and content. The model already supports multiple languages — Chinese, English, Japanese, and Korean — with more in the pipeline.

Also Read:

Southeast Asia is also on the radar. “Some partners have invited us to join Singapore,” Tao says. It is not a formal announcement, but it is not a non-answer either.

Also Read: Faster tech, slower brains: The biological blind spot of the AI race

Behind the product is a team that reflects the company’s unusual intersection of disciplines: Tsinghua-trained PhDs who specialise in GPU chip generation and also happen to be singers; professional musicians from Shanghai Conservatory of Music and Guangzhou Xinghai Conservatory, brought in specifically to evaluate whether the model’s output clears the bar a human ear would set.

It is, in its own way, a mirror of Tao himself, a machine learning veteran who spent three years training a model not because a market research report told him to, but because he wanted to give his wife a song she would remember.

The post Yinchao’s millions: AI music that lets anyone be a composer appeared first on e27.

For a while, differential privacy was spoken about as though it might do for data privacy what encryption did for data in transit. A hard technical answer to a messy institutional problem. The theory was elegant, the guarantees were rigorous, and the early signal from major adopters was powerful. 

That gap matters because it tells us something larger about technology strategy. Differential privacy did not stall because the mathematics was weak. It stalled because organisations kept treating it as a universal privacy answer when it is really a precision instrument for a narrower class of problems. It is very good at answering one hard question. How do you release useful aggregate information while limiting what can be learned about any one person? That is not the same as solving privacy in the round. 

It solved a narrower problem than the market wanted

Most organisations do not actually need a mathematically formal guarantee for every privacy question they face. They need a working combination of access controls, minimisation, retention discipline, contractual restrictions, governance, and operational trust. Differential privacy sits inside that world. It does not replace it. The mistake was to imagine that a strong formal guarantee at the output layer could make the wider privacy problem feel settled.

In practice, most institutions still need to govern collection, purpose, access, sharing, deletion, model use, and accountability separately. That is why differential privacy often ends up as a specialised control rather than the centre of the privacy operating model.

This is also why the technology feels simultaneously important and oddly non dominant. It addresses a real problem, but not the whole one. Strategists often back technologies that appear to simplify governance. Differential privacy usually does the opposite. It sharpens one guarantee while leaving the surrounding organisational obligations very much alive. That makes it more honest than many privacy narratives, but also harder to sell as a universal answer.

Also Read: How to build customer trust with improved data privacy

The trade off is too visible to ignore

Differential privacy makes privacy expensive in a way organisations can actually see.

The noise added in the wrong way can either weaken privacy or make the data less useful. Leader need help understanding the trade offs inherent in differential privacy. Its earlier discussion of open challenges goes further and says broader use will require better processes both for measuring utility and for helping users work with differentially private outputs. That is the part many executives dislike. Differential privacy does not let them pretend privacy is free. It forces an argument about how much accuracy, granularity, or downstream usefulness they are willing to give up.

In other words, differential privacy did not fail commercially because it was too academic. It failed to become ubiquitous because it was too honest. It puts the privacy utility bargain in plain sight. In large institutions, that usually means politics. Product teams want fidelity. Analysts want details. Revenue teams want more segmentation. Policy teams want stronger protection. Noisy outputs are not only a technical choice. They become a budget, power, and accountability conversation. 

Epsilon is mathematically neat and managerially awkward

Differential privacy relies on parameters that matter deeply and are still difficult to explain outside specialist circles. There is still no consensus answer on what epsilon should mean in practice or how it should be set. 

That is not a minor educational problem. It is a strategy problem. A control rarely becomes ubiquitous when the key parameter cannot be translated cleanly into board-level language, regulator language, procurement language, and customer language all at once. Differential privacy is strong where the institution can tolerate technical nuance and invest in interpretation. It struggles when leaders want a simpler sentence than the truth allows.

The engineering burden is still heavier than the story suggests

Differential privacy is easy to describe and hard to implement well. That should not surprise anyone who has actually watched privacy technologies move from paper to production. The hard part is rarely just the mechanism. It is the surrounding system. Contribution limits, privacy accounting over time, query controls, data schemas, public versus private feature choices, and monitoring all need to line up. This is not plug-and-play privacy. It is a design-heavy privacy.

Also Read: Data privacy for startups: Simple steps to protect sensitive documents

It asks for governance maturity that many firms do not yet have

Differential privacy is not just a maths layer. It is a governance discipline masquerading as a technical feature.

Explaining the protections to end users and other stakeholders is difficult because the guarantees are not absolute and need contextualisation. This is one of the most under-appreciated barriers to ubiquity. Differential privacy demands that an organisation know what data is being used, what counts as a contribution, what is being released, who decides the privacy budget, how utility is evaluated, and who signs off when those choices carry consequences. Many firms still are not good at that level of definitional discipline.

That is why differential privacy often lands best in institutions that already think like stewards rather than extractors. Official statistics agencies, mature research environments, and large platforms with dedicated privacy infrastructure can absorb the overhead. A typical enterprise trying to move fast with fragmented data ownership usually cannot. The challenge is not only whether it can add noise correctly. It is whether it can define responsibility clearly enough to use the technology honestly.

It becomes politically hardest where it matters most

The utility loss from differential privacy can fall harder on underrepresented groups, both in private data summaries and in differentially private machine learning. In plain terms, the smaller or less represented the subgroup, the more likely the noise is to hurt the usefulness. That makes deployment especially delicate in precisely the settings where fairness, public accountability, or high-stakes decisions matter most.

This is one reason differential privacy remains easier to justify in some telemetry and aggregate analytics settings than in high-consequence operational systems. A technology does not become ubiquitous just because it is principled. It becomes ubiquitous when the trade-offs are politically boring. Differential privacy is not there yet. In many important contexts, it still makes the distribution of cost too visible.

Also Read: How to unlock possibilities through data privacy enhancing technologies

So what is really going on

The more strategic reading is this. Differential privacy was sold as a privacy solution, but it behaves more like a discipline of institutional restraint.

It forces organisations to answer questions they would often prefer to blur. What are we actually trying to learn? How much precision do we truly need? Who gets to decide the privacy loss? Which users bear more of the utility cost? What other controls still matter because differential privacy does not solve them? Those are healthy questions. They are also exactly the sort of questions that stop a technology from becoming frictionless and universal.

So the right conclusion is not that differential privacy is disappointing. It is that the market misunderstood what kind of success it was likely to have. Differential privacy was never going to become ubiquitous in the simplistic sense of appearing everywhere sensitive data appears. It is becoming something else. A serious control for specific settings where aggregate insight matters, formal guarantees matter, and the institution is mature enough to live with visible trade-offs. 

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About a year ago, I started noticing a pattern in how clients were briefing us. They don’t just come to us for deliverables anymore. They were coming with questions. Which AI tools should we be using? Should we be using them at all? What does a responsible AI-assisted creative process even look like?

I spent seven years in government service before founding CreativesAtWork 14 years ago. Across those two very different worlds, I have sat across from a lot of different kinds of clients. But this was new. They were not just buying a service. They were looking for someone to think alongside them — someone who understood both the craft and the tooling, and could help them make sense of a landscape moving faster than anyone expected.

That shift told me something important: the gap isn’t just between AI and creatives. It’s between what the market now expects and what our talent pipeline is actually producing.

What clients are actually asking for now

The ask has quietly changed. Clients today don’t just want execution. They want judgment. They want someone who can look at an AI-generated concept and tell them honestly whether it’s on-brand, culturally appropriate, or legally murky. They want a creative partner who knows when to use the tools and, just as importantly, when not to.

That is a fundamentally different profile from what most creative training — formal or informal — has historically built toward.

And yet the conversation in most education and training circles is still centred on outputs: learn the tools, build the portfolio, get the brief done. The advisory layer — the ability to help a client think through AI adoption in their own creative process — is barely on the curriculum and it should be.

Also Read: What to actually prioritise when your board wants AI and everything feels urgent

Where the pipeline is falling short

Across Southeast Asia’s creative economy, which includes a large and growing freelance workforce, this mismatch is structural. Universities producing design, communications, and media graduates are still largely running programmes built for a pre-generative-AI world.

Bootcamps have moved faster, but they are largely targeting engineers, marketers and tools-specific and focused. Few focused on the mindset shift and the design thinking process together with the client.

The freelance community is largely left to self-educate. Platforms like Coursera and LinkedIn Learning have added GenAI modules, but most of what exists treats AI as a feature to understand rather than a professional context to navigate.

What actually would help

Peer-led communities are filling the gap faster than institutions. Regional design communities, Discord servers, and creative Slack groups are where working freelancers are sharing real workflows, catching mistakes, and pressure-testing AI output against live briefs. The learning is messy and informal, but it is grounded in actual work, which is more than most formal programmes can say.

Agencies that have restructured around AI are also quietly becoming training grounds. When the workflow changes, junior people inside it develop judgment faster — because they are doing real work, not doing exercises. That will be increasingly important, and how a good creative training will work. The tools are new. The apprenticeship logic isn’t.

The structural barriers are slowing progress

Three things are holding this back at scale.

Curriculum cycles move too slowly. Degree programme updates will take a while. GenAI capabilities are shifting every six to twelve months. It needs to catch up faster.

Assessment needs to be caught up as well. It is still hard to tell whether someone can genuinely work well with AI or is just using it to cut corners. Without clearer signals of quality, the market will not be able to reward real capability development the way it should.

Also Read: Think with AI: The new skill for social entrepreneurs

And the freelance workforce has no institutional home. Salaried workers access employer-sponsored training. Freelancers largely is on their own. In Singapore, SkillsFuture credits help, but take-up among creative freelancers stays low — partly because the available courses aren’t yet matched to how creative AI work actually operates day to day.

What needs to change

I would think an actionable next step for creatives is to have an industry body to lead the development of a portable, competency-based credential for AI-assisted creative work — built with practising freelancers, not just academics. One that assesses judgment and workflow design, not just tool familiarity.

For business leaders, rewrite your briefs. If you need someone who can direct AI-assisted production and advise on which tools fit the project, say that clearly. Vague calls for “AI-literate creatives” produce vague results.

And for educators: spend time with freelancers who are actually doing this work. The curriculum you need already exists in practice. It just hasn’t been written down yet.

The creative economy has always run on a mix of formal training, peer learning, and hard-won experience. What’s changed is how fast the ground is moving underneath all three.

Twenty-one years across two very different sectors has taught me one thing consistently: the people who stay relevant are not the ones who master the tools best. They are the ones who never stop asking what the work is actually for. That skill no model generates. But we need to start teaching it now.

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

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Eight years ago, I made a hiring decision that didn’t follow the usual rules. In today’s AI era, where tools can already assist with much of the work that we do, that decision feels even more relevant in hindsight. I was hiring for a fast-growing fintech company. The shortlist was what you’d expect: candidates from top universities, strong academic records, and big brand internships. On paper, most looked solid.

But one candidate stood out for a very different reason. He didn’t have a strong degree. But when we gave him a problem to solve, something clicked. He broke it down clearly, asked the right questions, and explained his thinking in a simple, structured way. He was fast, practical, and very focused on outcomes.

Our HR process didn’t allow us to hire him directly. We had to get special approval from leadership. We hired him anyway.

Within a few weeks, it was clear we had made the right call. He delivered faster than many experienced hires, not because he “knew more,” but because he could think clearly and adapt quickly. That moment changed how I look at talent.

What AI is changing right now in hiring

Today, that experience feels even more relevant. AI is now doing a lot of the work that used to help candidates stand out, right from basic research, writing first drafts, building summaries, and even structuring analysis.

We are seeing these changes across industries and, more importantly, across our own client ecosystem, where AI adoption is already becoming operational, not experimental.

For instance, BASIC Home Loan (a leading fintech) uses AI to simplify home loan journeys and reduce friction in early-stage processing, while KhiladiPro (sports tech startup), uses AI to make sense of large volumes of sports and engagement data. Both cases reflect something important: our clients are not just using AI; they are becoming AI-native in how they redesign workflows. And that changes what we value in people.

Also Read: What to actually prioritise when your board wants AI and everything feels urgent

This is not just a company-level shift. It is showing up in the broader labour market as well. The India Skills Report 2026 reinforces this transition: India’s employability stands at 56.35 per cent, even as demand rises sharply for digital and AI-related skills. India contributes about 16 per cent of global AI talent, positioning it as a key global supply hub; and by 2027, the country is expected to have 1.25 million AI specialists.

Taken together, the signal is clear: AI is not just changing tools inside companies, it’s changing how work is structured, and how talent is defined.

So what does a future-ready skill stack look like?

The hiring question has shifted. It is no longer: “Who has the best profile?” It is now: “Who can use new tools to think better and work faster?” Across teams, three shifts are becoming very clear—skills matter less than learning speed, thinking has become the real edge, and execution is now human + AI rather than either/or.

If that is how work is changing, the skill stack is also changing—from isolated abilities to a few connected strengths that work together.

• Industry knowledge + AI fluency: Understanding your sector’s economics, policy shifts, and blind spots is still important, but it now works best with strong AI fluency. The real edge is knowing how to use AI to speed up work, improve decisions, and sharpen thinking—not just to generate output.
• Thinking, data and storytelling: AI can process data, but it cannot decide what matters. The ability to find signals in data, simplify complexity, and turn it into clear, decision-ready storytelling is what helps people influence outcomes.
• Collaboration + communication: As more work becomes AI-assisted, human skills matter more in how teams align and move together. The ability to build trust, persuade, and coordinate across people is becoming a key leadership skill.
• Creativity: Automation improves efficiency, but it does not replace originality. In fact, as execution becomes easier, the ability to think differently becomes even more valuable.

Also Read: Think with AI: The new skill for social entrepreneurs

If I were hiring today

I would still make the same decision I made eight years ago. But I would add one more filter. Not “Which college?” Not “Which degree?”

Instead, I’d ask: Can this person learn continuously? Can they adapt as technology changes? Can they use AI to improve how they think and solve problems? Can they combine human judgment with machine capability? Because that is increasingly the real job.

Final word

The biggest talent shift in the AI era is not that degrees matter less. It’s that learning speed, adaptability, curiosity, and AI fluency now matter more than ever before. The strongest teams of the future will not simply be defined by credentials or polished resumes. They will be defined by how quickly they can learn, evolve, and work alongside AI to create meaningful outcomes.

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

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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There is a number buried in the Agentic Economy Report by blockchain firm Morph that should concern every payments executive in Asia: US$0.20. That is the average size of an x402 agent payment as of March 2026, according to on-chain analytics firm Lookonchain. It is also below the fixed per-transaction floor of every major card network on the planet.

Card rails were engineered for human-sized purchases. A US$0.20 payment — the kind an AI agent makes when it calls an API, queries a data source, or pays for a single unit of compute — is not economically viable on Visa or Mastercard. It is not that the networks choose to exclude this transaction category. It is their cost structure that makes it impossible to serve.

Also Read: The invisible shopper rewriting Asia’s e-commerce playbook

This is the core insight behind the Morph report’s Prediction 2: most agent-initiated payments, by transaction count, will settle outside traditional card rails.

The x402 protocol and Asia’s stablecoin moment

The mechanism enabling this shift is x402, an HTTP 402-based protocol for stablecoin micropayments at API call time. The standard was co-developed by Coinbase, Cloudflare, and the x402 Foundation, and Google’s AP2 added a native x402 extension for stablecoin settlement when it launched with 60-plus partners in September 2025. The protocol is elegant in its simplicity: when an agent requests a resource, the server responds with a 402 (Payment Required) status, the agent pays in stablecoin, and access is granted — all without a human card number, a payment gateway, or a merchant acquirer taking a slice.

The current volume is modest, but the trajectory is being watched carefully. Bloomberg, citing x402.org data, reported approximately US$24 million in 30-day x402 agent payment volume in early 2026. Andreessen Horowitz’s crypto research team placed the wash-trade-filtered figure at roughly US$1.6 million over the same window, a 15-fold gap that the Morph report acknowledges directly, noting that raw volume is not yet a reliable indicator of commerce. What is reliable, it argues, is the speed of protocol integration at major payments, retail, and platform companies.

For Southeast Asia and broader Asia Pacific, where cross-border payment friction remains high, and stablecoin adoption has grown fastest among the world’s unbanked and underbanked populations, the structural opportunity is substantial. Circle’s USDC Gateway is already built to batch thousands of micropayments before on-chain settlement, a design choice, the report notes, “that is only useful when machines, not people, are the senders.”

The agent overtakes the human

Prediction 3 in the Morph report is the most structurally significant for the stablecoin sector: AI agents will overtake humans in commercial stablecoin payments by transaction count. The claim is deliberately narrow; it is about machines paying for real goods, services, or API capacity, not human transfers or conventional trading bots.

Also Read: Stablecoins are becoming ‘dollars as a service’ for emerging markets

The case rests on two diverging trend lines. Human commercial stablecoin payments — people buying goods with USDC or USDT — are bottlenecked by merchant acceptance, retail onboarding, and regulatory uncertainty. They compound slowly, year over year, at a rate of low-to-mid millions of transactions globally. Agent commercial payments start from near zero and compound monthly. Three drivers make this credible.

MCP SDK installs grew from 5 million monthly in February 2025 to 97 million monthly by March 2026, putting agent tooling in the hands of virtually every enterprise developer worldwide. ERC-8004’s live deployment on the Ethereum mainnet provides agents with a portable identity and on-chain reputation not owned by any single platform, removing a key barrier to agent acceptance at the merchant layer. And the Bank for International Settlements, in Working Paper 1310, concluded that routine cash-management tasks could be automated using general-purpose large language models at institutional quality.

Tether’s CEO, Paolo Ardoino, has publicly set the endpoint at “one trillion AI agents” transacting on-chain within 15 years. The technical ceiling, the Morph report argues, is gone. “The commercial ramp is what is left.”

The institutional layer is the harder problem

For Asia’s banks, payment networks, and fintech infrastructure providers, the more pressing question is not whether stablecoins will carry machine-to-machine volume — that outcome is increasingly structurally inevitable — but whether they will build the institutional rails to handle it under compliance regimes.

JPMorgan’s Kinexys platform now processes US$5 to US$7 billion daily and has launched USD deposit tokens on Base. Project Agorá, co-ordinated by the BIS, is in testing with seven central banks on a tokenised, programmable cross-border rail. Fed Governor Christopher Waller delivered four public speeches on agentic AI in payments between September 2025 and February 2026.

Also Read: The growing adoption of Ethereum in emerging markets

The report quotes Richard Astle, VP Head of Network at Fireblocks, framing the institutional challenge precisely: “Letting an agent transact on behalf of a user, at scale, and under a real compliance regime is a question of how you delegate authority without delegating custody, how you enforce policy at agent speed, and how you reach end users without rebuilding the stack underneath them.”

For Singapore, which has positioned itself as Asia’s digital asset regulatory sandbox, and for the broader region’s central banks navigating their own stablecoin frameworks, this is the operative design question of the next 24 months. The volume is coming. The question is which rails it clears on.

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