For years, the travel industry focused on removing friction from the booking experience. And in many ways, it succeeded.

Flights, hotels, airport transfers, restaurant reservations, and activities can now be researched, compared, and confirmed in minutes. What once required printed itineraries, phone calls, or multiple agents now happens across a handful of tabs and apps. Travel planning has become faster, more intuitive, and more personalised than ever before. Global online travel bookings have now crossed the US$1 trillion mark, and the digital travel market is expected to grow to between US$1.4 trillion and US$1.6 trillion by the early 2030s.

This rapid growth reflects just how seamless the booking experience has become. But it has also created a new assumption: that once a trip is booked, the rest of the experience will simply work.

That is no longer true.

The real bottleneck in modern travel is not booking. It stays operational once the trip is in motion.

Today’s traveller is expected to function in real time. They are not just moving between destinations. They are navigating airports through app alerts, coordinating arrivals through messaging platforms, finding hotels through maps, adjusting plans through airline notifications, unlocking rides through transport apps, and paying digitally in unfamiliar places. Travel has become increasingly real-time, and that experience depends on being connected. According to IATA, 78 per cent of passengers now expect to use their smartphones for booking, payment, and navigating the airport experience.

This is a major shift in where travel friction actually lives.

The industry has spent years refining the front end of the journey. Searching, comparing, and buying are smoother than they used to be. But once the traveller lands, boards, reroutes, waits, or changes course, the pressure moves elsewhere. It moves to access. Can the map load? Can the message go through? Can the airline app refresh? Can the traveller receive the gate change, pull up the hotel address, reach a driver, or access the payment tool they rely on?

Also Read: Corporate travel in Southeast Asia was never broken, it was never built

Even short gaps in connectivity now create outsized disruption because so much of the travel experience is built on responsive, app-based behaviour. A missed update is no longer a minor inconvenience. It can delay a pickup, complicate a check-in, affect coordination with friends or family, or create confusion in moments when travellers need clarity most. This reliance is reflected in the rapid growth of travel apps, with global travel app revenue surpassing US$1.2 billion in recent years as travellers increasingly depend on mobile tools throughout their journeys.

That is why connectivity should no longer be treated as a travel add-on. It has become part of travel infrastructure. 

This is where technologies like eSIM are reshaping expectations. By enabling travellers to activate mobile connectivity instantly without relying on physical SIM cards, eSIM solutions reduce one of the most common points of friction in modern travel. Instead of searching for local SIM vendors or relying on inconsistent public Wi-Fi, travellers can stay connected from the moment they land, maintaining access to the tools they depend on throughout their journey.

But connectivity today is not just about getting online. It is about staying reliably connected in ways that match how people actually travel. That includes having access to essential apps even when data runs low, so travellers can still navigate, message, or access critical services without interruption. It means being able to share a hotspot with travel companions, ensuring that groups can stay coordinated without juggling multiple connections. It can also include added protections such as VPN access, helping travellers use public networks more securely while on the move.

The most useful travel solutions today are not always the most visible. Often, they are the ones who quietly keep the trip functioning. They remove friction in the background, support continuity, and help the traveller stay capable when the itinerary stops being linear. In practice, that means reducing the number of points where the journey can break.

Also Read: The AI travel revolution: Why hotels must be found by bots to be chosen by humans

This is especially relevant as travel becomes more dynamic. Plans change mid-route. Delays cascade. Travellers book later, adjust faster, and depend more heavily on digital tools while moving. The trip is no longer something managed only before departure. It is constantly being updated in motion.

That creates a different standard for what travellers need from connectivity. Being operational means being able to navigate, communicate, verify, pay, rebook, and adapt without losing momentum. It means the traveller can stay responsive when the situation changes. That may sound simple, but in practice, it is one of the most important forms of travel confidence.

This is where the conversation around mobile access needs to evolve. For too long, connectivity in travel has been framed mainly around convenience or cost. Those points still matter, but they no longer capture the full picture. The larger issue is continuity. When connectivity fails, travel does not just become less convenient. It becomes less functional.

That is also why solutions in this category need to be designed around real traveller behaviour, not just technical provision. Travellers do not think in terms of data alone. They think in terms of outcomes. Can they get where they need to go? Can they stay in touch? Can they handle the next change without friction?

Alongside reliability, predictability matters too. Unexpected roaming charges or bill shock can quickly turn a smooth trip into a stressful one. Modern connectivity solutions are increasingly designed to remove that uncertainty, giving travellers clear control over their usage and costs so they can focus on the journey itself.

The value is not just that travellers can get online, but that they can stay responsive as plans move. Whether that means accessing essential apps, managing movement on arrival, or staying connected through unexpected changes, the role of connectivity is increasingly tied to the traveller’s ability to keep the trip intact.

The travel industry has already made major progress in helping people book with ease. The next challenge is helping them move with confidence.

Because in modern travel, the hardest part is often not making the booking.

It is staying fully functional after the journey begins.

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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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A government-commissioned study found that 24 per cent of Global Business Services roles in Malaysia are highly impacted by AI and 65 per cent are medium impacted. Nearly nine in ten GBS jobs are changing in some material way within the next three to five years. The same study put a number on the people involved: 59 per cent of the GBS workforce, around 163,000 employees, need upskilling to stay relevant in roles that are evolving faster than the job descriptions written for them.

That figure sits against a harder one. Business closures and downsizing have already cost Malaysia tens of thousands of jobs this year. The Human Resources Minister’s position has been measured: AI isn’t the primary driver of those losses today, and workers who build AI skills won’t be left behind. That’s reasonable. What it doesn’t settle is who’s responsible for building those skills, and whether companies are doing it.

The data suggests most aren’t. Only 37 per cent of organisations have active internal AI training programmes running, based on research AGOS Asia conducted with Roland Berger, published in September 2025. The other 63 per cent are leaving it to individuals or waiting for a better moment to invest. With 163,000 GBS roles already on a clock, that gap is a serious one.

It shows up in a specific place. The job descriptions companies are hiring against today were largely written before generative AI was a daily work tool. Most have been updated at the margins, a line about digital proficiency here, a mention of system experience there, and that has been treated as current. It isn’t. A job description built around a fixed set of tools is quietly signalling the wrong priorities to every candidate who reads it.

The bar isn’t that every person becomes a technologist. It’s that they have enough familiarity with the tools in their environment to work alongside them confidently, to know when an automated output needs questioning, and to contribute to conversations about how a process could work better. That’s a realistic expectation.

Also Read: Are you a human resource?

But it doesn’t happen by accident, and it doesn’t show up in a job description that hasn’t been touched in three years.

The qualities that actually determine whether someone can work effectively alongside AI, learning orientation, adaptability, and willingness to question automated results, rarely appear as real evaluation criteria. They sit in a paragraph about culture, and nobody tests for them in the interview.

Three questions hiring managers can use now to surface whether a candidate has the mindset the next three years will require.

• One: “Tell me about a process you changed without being asked to. What prompted it, and what did you do?” This separates people who treat improvement as part of their job from those who wait for instruction.
• Two: “Describe a time you had to learn a new tool or system quickly. How did you approach it, and what would you do differently?” This distinguishes people who adapt by instinct from those who need a formal programme before they’ll move.
• Three: “How do you stay current with changes in your field? Give me a specific example from the last three months.” The three-month constraint matters. It makes vague answers visible immediately.

Also Read: Human resources hacks for the bootstrapped startup

Hiring is only half of it. The obligation runs in both directions. Rewriting job descriptions without investing in people already in the function creates a split: new hires arrive with the right profile while experienced team members find themselves measured against criteria they haven’t been supported to meet. That shows up in retention before it shows up anywhere else.

The same TalentCorp study names talent retention and development as one of its core recommendations for industry players 5i, not a nice-to-have. Russell Parry at AstraZeneca built that thinking into their modular AI training programme from 2023: “We have seen measurable gains in both productivity and retention since rolling out our modular training approach. People want to work where they are being invested in, and they want to work on things that feel like the future.”

Most companies are measuring productivity. Fewer are measuring whether their people investment is affecting whether people stay. The 163,000 figure is a policy problem and a company problem at the same time. What happens inside individual organisations, at the level of the job description, the hiring conversation, and the performance review, is still a corporate decision. One won’t solve the other.

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A sharia bank in Jakarta asked me last quarter whether their new credit scoring model needed Sharia Supervisory Board review. They had deployed it three months earlier, trained on five years of their own portfolio data, and were preparing to roll it out across their consumer financing book. The risk committee was satisfied. Nobody had asked the sharia supervisory board.

That conversation is the part of Islamic finance’s AI story we have not yet written honestly.

I have spent four years inside sharia risk policy at Indonesian banks, and fifteen years across the country’s financial functions. The structures that govern sharia finance — fatwa, supervisory boards, classical jurisprudence applied to modern instruments — are robust at what they were designed to govern. They were not designed to govern algorithms that retrain themselves quarterly. The gap between what these institutions can review and what their banks are deploying is widening faster than anyone inside the system is willing to name.

I would call it the fatwa lag.

The structure that worked

Every sharia banking product, across the major Islamic finance markets, must pass through a formal sharia review before launch. In Indonesia, that means a fatwa from the National Sharia Council (DSN-MUI). At the institution level, every sharia bank operates with an independent Sharia Supervisory Board (DPS) that reviews products, contracts, and operational practices against classical jurisprudence.

The system has, for decades, worked. It has prevented riba from creeping into modern Islamic banking products. It has flagged gharar — excessive uncertainty — and maysir, speculation, inside derivative-like instruments that conventional finance accepted without question.

What it has not faced before is a class of products that change their own logic between fatwa hearings.

Also Read: In SEA, Millennial Muslims in Indonesia are more confident about using AI for travel: HHWT

Where AI breaks the system

Three problems are emerging quickly enough to deserve naming while there is still time to design around them.

The black-box gharar problem. Sharia explicitly prohibits gharar in contracts and transactions. When a customer is denied financing by a machine learning model that nobody at the bank can fully explain, the basis of the decision is opaque. Conventional finance has been wrestling with this through model explainability tools. Sharia finance faces a sharper version of the same question: at what level of opacity does a decision become non-compliant by virtue of the uncertainty alone?

The fatwa cycle versus the model cycle. A new sharia banking product typically takes six to eighteen months to receive a DSN-MUI fatwa. A credit model can be retrained quarterly, sometimes monthly. The current version of the model is therefore almost never the version that received scholarly review. The bank assumes the principle approved in the original fatwa survives across retraining cycles. In many cases, it does. In some cases, it cannot.

The board capacity gap. Sharia Supervisory Boards across ASEAN are composed of distinguished scholars — masters of classical jurisprudence, often with limited exposure to model architectures, training data biases, or drift monitoring. The review process was designed around contracts, not statistical artefacts. Asking these boards to certify AI-driven products in their current form is asking them to review what they were never trained to read.

What is starting to happen

A few institutions are quietly responding.

Joint sharia-and-model reviews. A small number of leading sharia banks now run parallel reviews — one by the DPS, one by the model risk function — and reconcile the two before deployment. The process is slow. It is also producing the most defensible decisions.

Bilingual practitioners. The most valuable people in this space are the ones with both sharia training and quantitative risk fluency. Universities in Indonesia, Malaysia, and the Gulf are beginning to design joint programmes, but the first graduates are years from sufficient seniority.

Conservative model design. Some sharia banks deliberately choose simpler, more explainable model classes for sharia products — accepting a small loss in predictive accuracy for the ability to defend each decision to the DPS. The institutions doing this do not advertise it publicly. It is the right instinct.

Also Read: Seasonal product cycles: Why some features only work at certain times

What the framework should look like

A serviceable AI compliance framework for Islamic finance would need at least three components.

A standing AI advisory protocol inside each Sharia Supervisory Board, with bilingual practitioners attached for technical translation. The classical scholarly authority remains on the board. The technical literacy that informs it sits beside.

A version-aware fatwa system. Rather than approving a model once at deployment, fatwas for AI-driven products should specify the boundary conditions under which the fatwa remains valid — training data scope, model class, performance envelope. Re-training inside those bounds requires no new fatwa. Re-training outside them does.

Cross-jurisdictional coordination. The DSN-MUI, the Shariah Advisory Council at Bank Negara Malaysia, and equivalents across the Gulf are wrestling with the same problem in isolation. A shared registry of approved AI compliance approaches, even at the level of guidance, would accelerate the system as a whole.

The macro stakes

Indonesia is the largest Muslim-majority economy in the world. Malaysia, Brunei, and the southern Philippines are growing sharia finance markets. The Gulf states host the deepest pool of sharia compliance scholarship globally. Each is now deploying AI inside financial services at the same pace as conventional banking — without the same maturity of risk infrastructure designed for the questions AI raises.

The Islamic finance system has spent forty years proving that principles can govern modern markets without being compromised. The next decade will test whether those principles can also govern markets that change their own logic between reviews. The institutions that answer that question first will set the standard. The ones that wait will inherit one.

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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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Daniel Kokotajlo used to lead research at OpenAI, thinking several years ahead. He left in 2023 and gave up a chunk of equity/money to do it because he didn’t want to sign away his right to talk about what he was seeing. Since then, he’s built out some of the more detailed public forecasts of where AI is headed, and people in the field take him seriously, not because he’s dramatic, but because he isn’t.

So when he told an interviewer recently that he and his wife decided not to have any more kids, it landed differently than it would from someone hyping a headline. His forecasts, he said, had “collapsed toward the present.” Too uncertain. He doesn’t expect his six-year-old daughter to ever join a traditional workforce.

I’m not writing this to argue whether he’s right about the timeline. Reasonable people in AI disagree hard on the specifics; some think 2027 is absurdly aggressive, some think it’s conservative. That’s not the interesting part.

The interesting part is that someone with more visibility into this than almost anyone, when it came down to planning his own family’s life, didn’t hedge with optimism. He priced in the uncertainty and made a real decision around it. That’s a different thing than doomscrolling about AI. That’s someone treating an unstable variable like an unstable variable, instead of pretending it’ll sort itself out because it always has before.

Now zoom out to where I actually spend my time, which is talking to founders, funds, and increasingly, final-year students across Vietnam, the Philippines, and Indonesia, who are a few months from graduating into whatever the job market turns out to be.

Also Read: AI and the crisis of recognition: Do we still see the human behind the words?

Nobody’s telling them the plan might not hold. Universities are still building curricula around stable career ladders. Parents are still saying, “Finish your degree, get placed, climb”. Job boards are still structured like 2015. Meanwhile, the entry-level roles that used to be the first rung, junior analyst, junior dev, first line support, are the exact roles AI tools are already eating fastest, because they’re the most repeatable, most pattern-based work in any organisation.

That’s not a hot take; that’s just what’s happening quarter over quarter. The uncertainty Kokotajlo is pricing into his family planning is the same uncertainty sitting underneath every “get a good job” conversation happening in a Vietnamese household right now. Nobody’s saying it out loud. The gap between what these systems assume and what’s actually shifting keeps growing, and almost nobody’s naming it directly to the 22-year-olds who are about to walk straight into it.

Here’s where I’d push back on the doom version of this story, though, because I don’t think the answer is fear, and I don’t think it’s “learn to code” either; that ship’s more complicated than it was five years ago.

What I’ve noticed talking to students across the region who seem the least anxious about this isn’t that they’re smarter or more technical than everyone else. It’s that they’ve stopped treating “get hired by one company” as the whole plan. They’ve got a small portfolio of things they can point to, a project, a client, a certificate that proves they actually did something rather than just sat through a syllabus. They’re building proof of work before anyone’s paying them for it. They think in terms of what they can do, not what title they’re hoping to get.

Also Read: The fatwa lag: How AI is overtaking the system designed to govern Islamic finance

That’s not a hack, and it’s not new either; honestly, it’s closer to how things worked before large stable companies existed, when people had trades and reputations instead of resumes. What’s new is how early you need to start building that muscle now, because the old runway, degree, then job, then promotion, is getting shorter every year, not longer.

I don’t think Kokotajlo’s daughter’s future is as bleak as the framing sounds. Six-year-olds have a long runway to figure out a very different world. Final-year students don’t have that same runway. They’re standing at the door right now.

So if even the people closest to how fast this is moving are hedging their own kids’ futures instead of assuming business as usual, that’s worth sitting with for a second before you finish your final semester assuming the plan you were handed still holds.

The students I talk to who aren’t waiting around for that plan to confirm itself aren’t scared. They’re just already building something of their own.

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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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Most product leaders are taught to watch competitors, benchmark features, and track market share as if category collapse begins with a stronger rival building a better version of the same thing. That story is tidy, but it is not how extinction usually works.

Entire product categories do not disappear because somebody made a slightly better product. They disappear because the conditions that made the category necessary stop holding. What looked like a stable market suddenly loses its reason to exist in its current form. The product may still work. The customers may still know the brand. The teams may still be shipping. Yet the category is already moving from essential to optional, and from optional to strange.

Categories die when their old logic stops making sense

Every product category is built on a deeper logic than the features it happens to include. That logic usually answers a few quiet questions. Why does this need to exist as a separate product? Why is this problem important enough to buy directly? Why should this workflow live in one place rather than another? Why is the current buyer the right buyer? Why does the category deserve its own budget, its own owner, and its own operational space inside the customer’s world?

Extinction begins when those answers weaken.

A category can look healthy on the surface while its underlying logic is already decaying. Usage may still be present. Revenue may still look respectable. Buyers may still renew because change is inconvenient. But if the market has started solving the same job through infrastructure, platforms, defaults, or adjacent products, the category is already in trouble. It is no longer being chosen because it is the best expression of the need. It is being tolerated because history has not finished moving yet.

The most dangerous extinction event is when the job becomes ambient

The cleanest way to understand category collapse is to ask what happens to the core job over time.

Some jobs become more specialised. Those usually create new categories. Others become more routine, more embedded, and less worthy of a standalone purchase. That is when extinction risk rises sharply.

A category is in trouble when the job it solves starts becoming ambient. By that I mean the job still matters, but users no longer want to visit a dedicated product, maintain a separate workflow, train a separate owner, or justify separate spend to get it done. They want the capability where the work already happens. They want it built in, quietly available, and increasingly invisible.

Also Read: Your customers are not buying your product, they are buying a better version of themselves

Extinction is usually caused by a shift in habitat, not a flaw in the species

Product people often describe collapse as if the incumbent product failed to evolve. Sometimes that is true. More often, the more revealing question is whether the habitat changed.

In product terms, habitat means the wider conditions that determine how value is created and captured. It includes distribution, buyer incentives, workflow location, data gravity, trust, regulation, integration expectations, and the cost of switching behaviour.

A category can be well designed for one habitat and completely ill-suited for the next. What made it successful can even become the very thing that slows adaptation. Deep control becomes friction. Rich configurability becomes overhead. Dedicated interfaces become needless travel. Specialist ownership becomes an organisational drag. Premium economics becomes harder to justify once the capability starts appearing inside broader platforms.

When a category’s language starts sounding old before its revenue does

One of the earliest warning signs is linguistic. Customers begin describing the problem differently. They no longer use the language that built the category. They speak in broader outcomes, adjacent workflows, or platform expectations. The old category terms start sounding internal, vendor-led, or historically specific.

Language is often the first place where market reality moves. Customers stop asking for the product as a noun and start asking for the capability as a verb. They do not want the category. They want the result. That shift is dangerous because it weakens the psychological boundary that kept the category intact. Once customers stop believing the problem deserves its own named product class, bundling becomes easier, substitution becomes easier, and the product’s claim to standalone importance starts eroding.

When the buyer changes, and the category does not

Many product categories are built around a particular buyer logic. A certain function owns the problem, controls the budget, and values the product for reasons tied to a specific era of operating reality.

Extinction risk rises when the economic buyer changes, but the category continues selling itself to the previous one.

This is not just a go-to-market issue. It is often a sign that the product category is losing its place in the organisation. The new buyer may want broader workflow coverage, lower tool sprawl, tighter integration, stronger governance, or simpler procurement. A category that once won by being excellent at one narrow job may now look misaligned with how decisions are being made.

Also Read: When AI leaves the screen, cybersecurity becomes product responsibility

When data and workflow gravity move somewhere else

Some categories exist because they sit close to the data and close to the action. They have natural gravity. The product is where the relevant information lives, where decisions get made, or where execution naturally happens.

If the most important data starts accumulating elsewhere, or if the primary workflow shifts into another environment, the category begins losing its natural advantage. It has to work harder to stay relevant because the customer’s day now begins somewhere else. The product becomes a destination rather than a native layer of work.

When the category starts defending the process rather than creating leverage

One of the clearest late-stage signals is rhetorical. Category leaders begin talking less about new leverage for customers and more about the seriousness, depth, and discipline of the category itself. They argue that the problem is too important to simplify, too complex to embed, or too specialised to become part of a broader product.

Sometimes that is true. Quite often, it is the language of a category defending its old boundaries.

This matters because healthy categories usually talk about expanding possibilities. Dying categories increasingly talk about why the old structure must remain in place. They frame change as recklessness. They equate simplification with naivety. They protect the category’s architecture more fiercely than the customer’s changing reality.

How to predict extinction before it becomes obvious

The most useful way to predict category death is to stop asking whether the product is still good and start asking whether the category still deserves to exist in the same place.

That requires a different discipline of observation.

You have to watch where customers want the capability to live, not just whether they still value the capability. You have to watch who now owns the decision, not just who owned it historically. You have to study whether the problem is becoming more standalone or more ambient. You have to notice when the market’s language shifts from tool choice to expected default. You have to look for cases where the distribution starts with overwhelming superiority. You have to identify when the product’s natural habitat has moved, even though the organisation has not.

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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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As we move deeper into 2026, the initial efficiency high of AI adoption is being met with a sobering reality: when everyone uses the same models to automate, the result is beige decay, a technically perfect but culturally hollow sameness. Recent findings from the iF Design Trend Report 2026 argue that we are entering an age of average, where algorithmic logic accelerates a globalised visual and strategic sameness.

For the modern organisation, the ultimate competitive edge is no longer how much you can automate, but how well you can protect and develop Human Intelligence (HI).

From information to high-consequence judgment

AI excels at probabilistic forecasting and pattern recognition across large datasets. However, Deloitte’s 2026 Human Capital Trends emphasise that Human Intelligence remains the dominant force in ambiguous, novel, or value-driven situations.

True HI is the ability to maintain cognitive readiness, the mental muscle required to make decisions when data is sparse or conflicting. In high-pressure environments, this isn’t just about skill; it’s about the behavioural readiness to override a machine-generated suggestion when it fails the vibe check of brand intent or ethical nuance.

Safeguarding the originality moat

If your talent development focuses only on prompting, you are effectively training your team to be interchangeable with the machine. Real growth in 2026 comes from recoupling design and Judgement. As noted at the recent Wall Street Journal Future of Everything Forum, companies that safeguard human intuition and creativity will gain a significant competitive edge as knowledge work becomes increasingly democratised.

Also Read: The great rotation: How AI stocks are stealing billions from crypto

Your originality moat is built when your team uses AI as a Junior Analyst but retains the role of Senior Partner. Development programmes should focus on:

• Critical interrogation: Training talent to deconstruct AI outputs to find the Perfect Flaw, those human idiosyncrasies that make a strategy feel authentic rather than automated.
• Ethical control: Ensuring that accountability remains a human function, especially in high-stakes decisions where math cannot replace meaning.

The structural sovereignty of talent

A common failure in 2026 is the cognitive divide, where leadership retains judgment while the rest of the workforce is relegated to automation. To avoid this, organisations must empower talent to act as project architects.

By leveraging a hybrid model, where the internal human loop owns the intent and an external build engine handles the execution, you allow your talent to stay in the high-value zone of design and strategy. This isn’t just an efficiency hack; it is a retention strategy. Talent in 2026 gravitates toward organisations that treat them as sovereign thinkers, not just prompt operators.

Conclusion: The strategic asset

Stop treating Human Intelligence as a soft skill. In 2026, judgment is your hardest-edged financial asset. As automation reduces the cost of doing, the market value of knowing what to do will continue to skyrocket.

The startups that win won’t be the ones with the best AI. They will be the ones who used AI to free their humans to be more original, sovereign, and intelligent than ever before.

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I couldn’t understand why my student had ignored almost all of my feedback. I had carefully reviewed his capstone presentation, working through each slide, thinking about the structure, the technical flow and how he could communicate his ideas more effectively.

Like many educators adapting to the AI era, I also used AI to help organise my comments and articulate my ideas more clearly. Not to replace my judgement, but to make the feedback easier for him to understand.

When presentation day arrived, however, very little had changed. As I sat through the presentation, I felt quietly disappointed. Not because the presentation was poor, but because I knew how much thought had gone into helping him succeed.

Only afterwards did he explain why. “Professor… I thought most of the feedback was generated by AI.”

I still remember that moment. Not because I felt accused, but because I suddenly realised he had never really judged the feedback itself. He had judged what he believed about the person behind it.

I explained that I had carefully reviewed his work, thinking through the arguments, deciding what to keep, what to remove and how best to help him tell a stronger story. He apologised. He admitted that during the presentation he could sense the disappointment on my face.

That conversation stayed with me. Not because of what my student had done, but because of what both of us had unknowingly assumed. He assumed polished feedback meant little human effort. I assumed genuine effort would naturally be recognised. Both of us were wrong.

Also Read: Singapore and Taiwan have a new window of opportunity, but will they seize it?

For generations, we have relied on visible signals to understand one another. A carefully written report reflected thoughtful analysis. A detailed email reflected commitment. Constructive feedback reflected invested mentorship. These signals were never perfect, but they helped us recognise something important: that another human being had cared enough to think carefully before responding.

Today, AI can generate many of those same signals in seconds. The technology is not simply changing how information is produced. It is changing how we interpret the people behind it. And that is a much bigger change than I first realised.

The more I reflected on the incident, the more I realised it was never really about education. Across workplaces, classrooms and public conversations, AI is changing more than how information is produced. It is changing how we interpret the people behind polished outputs.

Managers question whether polished reports reflect genuine judgement. Employees wonder whether feedback reflects careful thought or automated assistance. Readers increasingly question whether articles, opinions and social media posts represent authentic human perspectives. In each case, the uncertainty is remarkably similar. We are no longer simply evaluating what people produce. We are trying to understand the human being behind it.

What surprised me most was not that my student questioned the feedback. It was that he questioned whether there had been a person behind it who had genuinely cared. That was the moment I realised something much larger than a classroom misunderstanding. AI had not made care disappear. It had made care harder to recognise.

Ironically, this experience has not made me less supportive of AI. Quite the opposite. I believe students should learn about AI, learn with AI and learn to use it responsibly. Avoiding AI entirely will not prepare them for the realities of future workplaces. Likewise, educators should embrace AI where it genuinely enhances learning, improves efficiency and supports better teaching.

The objective is not to protect old ways of learning. It is to preserve what matters most within them.

Also Read: Architecting the future: A strategic guide to building an internal AI academy

If AI can increasingly generate fluent outputs, fluency alone can no longer serve as evidence of learning. Information is becoming easier to generate than ever before. What matters increasingly is what people do with it. Can they exercise judgement? Can they challenge assumptions? Can they navigate uncertainty? Can they make sound decisions when there is no obvious answer? These are qualities that no technology can simply generate on demand. They develop through experience.

This is one reason I continue to value authentic learning environments. When students work on real projects, collaborate with industry partners, navigate operational constraints and confront unexpected outcomes, they quickly discover that reality rarely follows a script. Assumptions fail. Teams disagree. Unexpected problems emerge. Decisions must be made with incomplete information. These experiences develop something that polished reports alone cannot reveal. Judgement. And judgement grows through reflection, mentorship, conversation and experience.

Perhaps this is why I no longer see AI simply as a technological challenge. It is also a human one.

Months after that conversation, I published an article about AI. As I watched readers respond, I found myself returning to the same question that had first crossed my mind. Would people assume this article had been written by AI too?

Today, that question no longer troubles me. What matters is not whether AI helped organise my thoughts. What matters is whether readers still recognise the human thinking, judgement and care behind the words they read.

AI can generate fluency. It can organise information. It can help us work faster than ever before. But perhaps the more important question is no longer whether AI helped produce the words before us. Perhaps it is whether we still take the time to recognise the human thinking, judgement and care behind them. Because meaningful learning has never been built on information alone. It has always been built on relationships.

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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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Malaysian fintech company CapBay has partnered the Malaysia Digital Economy Corporation (MDEC) to offer growth financing to Malaysia Digital-status technology companies, in a move aimed at widening access to debt capital for startups and scale-ups that often fall outside conventional bank lending criteria.

The MD Technology Financing Programme is backed by a RM200 million (~US$47.1 million) financing pool. Eligible companies may apply for financing of up to RM3 million (US$707,000), with repayment tenures of up to 60 months, interest rates starting from 6 per cent per annum, and a six-month repayment grace period.

Also Read: The SME finance reset: 3 steps to fix what’s breaking your growth

The programme is open to established technology businesses as well as early-stage and pre-profit startups. Companies incorporated for as little as six months may apply through CapBay’s digital platform, provided they hold Malaysia Digital status.

For Malaysia, the initiative sits at the intersection of two policy priorities: improving startup access to growth capital and accelerating the country’s AI Nation 2030 agenda. For Southeast Asia, it reflects a broader shift in how governments, fintech lenders, and development agencies are trying to plug financing gaps as venture funding becomes more selective.

Debt fills part of the startup funding gap

The timing is significant. Southeast Asia’s startup funding environment has cooled sharply from its 2021 peak, forcing founders to extend runway, cut burn, and explore alternatives to equity rounds. Google, Temasek, and Bain & Company estimated Southeast Asia’s digital economy gross merchandise value at US$263 billion in 2024, but funding into the region has remained under pressure as investors prioritise profitability and unit economics over rapid expansion.

That shift has made debt financing more relevant, particularly for companies with recurring revenue, signed contracts, government-linked projects, or receivables that can support repayment. In markets such as Singapore, Indonesia, and Vietnam, SME and startup credit providers including Funding Societies, Validus, Aspire, and other alternative lenders have expanded by underwriting businesses that banks traditionally view as too young, too asset-light, or too risky.

Malaysia has followed a similar path. The country’s peer-to-peer financing sector is regulated by the Securities Commission Malaysia, and platforms such as CapBay, Funding Societies Malaysia, and other SME-focused lenders have become part of the financing stack for small businesses. The difference with the MDEC-linked programme is that it specifically targets Malaysia Digital companies, many of which rely on intellectual property, software, talent, and proprietary systems rather than physical collateral.

CapBay said its credit assessment model uses artificial intelligence (AI) to evaluate applicants based on business fundamentals and growth potential rather than hard assets. That approach may help more software and technology companies qualify for financing, though underwriting early-stage companies remains difficult, particularly when revenue is uneven or customer concentration is high.

Public-private capital for digital policy goals

MDEC’s involvement gives the programme a policy dimension. The agency, which sits under Malaysia’s Ministry of Digital, leads the Malaysia Digital initiative and has been positioning the country as a regional base for AI, digital services, and technology investment.

Also Read: Choco Up moves deeper into supply-chain finance as SMEs battle delayed payments

Malaysia’s digital economy has already become a sizeable part of the national economy. The government has previously targeted digital economy contribution of 25.5 per cent of gross domestic product by 2025, while regional competition for AI investment, data centres, cloud infrastructure, and tech talent has intensified across Singapore, Indonesia, Thailand, and Vietnam.

For MDEC, improving access to financing is part of keeping Malaysian companies competitive beyond grants, incentives, and ecosystem branding. Many tech firms can raise small seed rounds but struggle to secure follow-on capital without giving up more equity. Debt, when used carefully, can provide working capital for hiring, product development, procurement, or regional expansion without further dilution.

Ang Xing Xian, co-founder and Group CEO of CapBay, said conventional credit frameworks often misread technology companies because their value is not tied to physical assets.

“The MD Technology Financing Programme addresses this by basing credit decisions on business fundamentals and growth trajectory rather than physical collateral, which aligns with how tech companies are actually structured,” he said. Ang added that opening the programme to startups from six months of incorporation gives young companies access to non-dilutive financing “at a stage where equity is often their only option”.

That is the central argument for the programme. But it also raises the usual caution around venture debt and startup loans: capital that does not dilute shareholders still has to be repaid. For pre-profit companies, debt can extend runway only if there is a credible path to revenue growth, predictable collections, or contract-backed cash flow.

CapBay’s lending track record

CapBay is not a new entrant to SME financing. Since 2016, the company says it has facilitated more than RM5.6 billion (~US$1.32 billion) in financing to over 2,600 enterprises. Its business spans supply chain finance and peer-to-peer financing, connecting businesses with banks and investors.

Supply chain finance has become an important alternative credit channel in Southeast Asia, where SMEs often face delayed payments, limited collateral, and inconsistent access to bank loans. In markets such as Indonesia and the Philippines, similar gaps have helped fuel embedded finance, invoice financing, and digital lending models, although regulators have also tightened scrutiny around risk controls, disclosures, and lender conduct.

For Malaysia’s technology companies, the MDEC-CapBay programme could be most relevant to startups that have moved beyond concept stage but are not yet attractive to banks or late-stage venture investors. These may include enterprise software firms, AI service providers, managed services companies, cybersecurity vendors, digital content businesses, and other MD-status firms with contracts but limited collateral.

The broader question is whether such programmes can scale without loosening credit discipline. Southeast Asia has seen enough fintech lending cycles to know that alternative underwriting is useful only if collections, default management, and borrower suitability are handled rigorously.

Also Read: Venture debt: How it stacks up against loans and equity

For now, the programme gives Malaysian tech companies another financing route at a time when equity capital remains selective and regional competition for digital economy leadership is rising. Its success will depend less on the headline size of the financing pool than on whether the capital reaches companies with real commercial traction — and whether they can repay it while growing.

The post CapBay, MDEC set up US$47M debt financing pool for Malaysian tech firms appeared first on e27.

Basketball has always been built for momentum. A fast break, a buzzer-beater, a chasedown block or a no-look pass can change the mood of a game in seconds. That rhythm has made the sport especially well-suited to the digital age, where fans increasingly discover, follow and debate sport through clips, personalities, live data and second-screen experiences.

For decades, the live broadcast was the centre of the basketball experience. Fans watched games from start to finish, followed their local team and waited for the next day’s newspaper or television highlights to relive the biggest moments. That model still matters, especially for marquee NBA games, playoffs and major regional tournaments. But the way younger fans engage with basketball is expanding far beyond the full match.

Today, basketball is not only consumed as a 48-minute game. It is experienced as a continuous stream of moments: a viral dunk on TikTok, a player’s tunnel outfit on Instagram, a fantasy debate on X, a YouTube breakdown of defensive schemes, a live-score alert during work or school and a group chat arguing over whether a rookie is already a franchise player. For many fans, this digital layer is not secondary to the sport. It is how they enter it.

Basketball’s advantage in the short-form era

Some sports struggle to translate cleanly into short-form content. Basketball does not. Its best moments are visually immediate and easy to understand, even for casual viewers. A three-pointer from the logo, a crossover that sends a defender stumbling or a last-second game-winner needs little explanation.

That has given basketball a natural edge on social media. The NBA has leaned heavily into this shift, turning its players and highlights into global content assets. The league’s digital reach is now part of its business model, not simply a marketing add-on. The NBA reported record social media engagement around recent international games, including the 2025 Paris Games, where League Pass viewership in France rose 29% compared with the previous year’s Paris Game. 

The league’s 2025-26 season also reflected how broadcast and digital growth are now working together. AP reported that the NBA recorded its highest opening-month viewership in more than 15 years, alongside more than 30 billion views of NBA content on social media and growth in League Pass subscriptions. 

The lesson is clear: short-form content is not necessarily replacing live sport. Done well, it can feed it. Clips create curiosity, personalities create loyalty and digital discussion keeps the league relevant between games.

Also read: How broadcast innovation in APAC is redefining the e-sports viewing experience

Players are becoming media channels

Basketball’s next generation of fans often follows players before teams. This is especially true for international fans who may not have a local NBA franchise but feel connected to individual stars. A young fan in Manila, Singapore, Jakarta or Kuala Lumpur may follow Victor Wembanyama, Luka Dončić, Stephen Curry or Caitlin Clark through highlights, interviews, fashion, training clips and behind-the-scenes content before becoming attached to a particular team.

This changes how basketball is marketed. Teams still matter, but player identity has become one of the sport’s strongest digital engines. The modern basketball fan does not only watch what happens on court. They follow workouts, sneaker drops, podcasts, fashion moments, gaming appearances and personal brands.

The NBA’s continued partnership with 2K is part of this wider ecosystem. The league and WNBA extended their global partnership with the NBA 2K video game franchise in 2025, covering the NBA, WNBA, G League and USA Basketball. Reuters described the agreement as part of a broader push to deepen fan engagement and extend the cultural reach of basketball through gaming. 

For younger fans, this is normal. They may first encounter a player through a video game, then follow them on social media, then watch highlights, then join live discussions, then eventually subscribe to a broadcast or streaming service. The funnel is no longer linear.

The second screen is changing the value of live games

The live game remains the premium product, but it is no longer watched in isolation. Fans now watch with phones in hand, using social media, live stats, messaging apps, fantasy platforms and sports content feeds at the same time.

This matters because attention is being split, but not necessarily lost. A fan checking box scores, player props, tactical commentary or injury updates during a game may actually be more engaged, not less. The second screen gives fans more ways to participate, especially when they are not sitting courtside or watching with a large group.

Research from GWI found that Gen Z sports fans are more likely than average to play mobile games and use social media while watching sport, creating new opportunities for real-time content, branded interaction and personalised engagement. 

For basketball, this behaviour fits naturally. The sport is stat-rich, fast-moving and discussion-friendly. Every possession generates data: points, assists, rebounds, shot charts, fouls, rotations, plus-minus and efficiency metrics. Fans do not have to wait until the final whistle to analyse the game. They can debate it possession by possession.

Also Read: From niche hobby to billion-dollar industry: The meteoric rise of esports

Asia’s basketball audience is digital-first

Basketball’s digital growth is especially relevant in Asia. The Philippines remains one of the world’s most passionate basketball markets, while countries such as Indonesia, Singapore, Malaysia, Thailand and Vietnam have growing communities around the NBA, local leagues, school competitions, streetball and content creators.

For many fans in the region, time zones make full-game viewing difficult. A weekday NBA game may take place during work or school hours. This makes highlights, recaps, live-score alerts and social clips even more important. Digital content allows fans to stay connected without always watching every game live.

That has commercial implications. Rights holders, leagues and brands cannot think only in terms of broadcast windows. They need to consider the entire fan journey: pre-game storylines, live engagement, post-game clips, player-led content, fantasy discussions, creator commentary and community-led debate.

Regional basketball scenes can also benefit from this shift. Local leagues may not have the production budgets of the NBA, but they can still build fan loyalty through consistent storytelling, player access, social-first highlights and mobile-friendly formats. A young player’s dunk in a regional league, if packaged well, can travel far beyond the arena.

The future fan may start with a clip, not a club

The next generation of basketball fans may not begin by choosing a team. They may begin with a moment. A highlight appears on their feed. A player’s personality catches their attention. A creator explains why a certain team’s offence is exciting. A fantasy discussion makes them care about a role player. A live update pulls them into the fourth quarter of a close game. This is the new fan pathway. It is fragmented, but powerful. Basketball is not losing its traditional audience. It is adding layers around it.

For leagues and sports businesses, the challenge is to connect these layers intelligently. The broadcast, the arena, the social clip, the data feed, the gaming experience and the second-screen platform should not be treated as separate worlds. They are all part of the same fan economy.

Basketball’s strength is that it already understands spectacle, personality and rhythm. In the digital era, those qualities travel further than ever. The court remains the centre of the sport, but the next generation of fans is being built everywhere around it: on phones, in feeds, across group chats and through the interactive platforms that keep the game alive long after the final buzzer.

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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.

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

The post How Asia is turning football’s year-round calendar into a fan engagement battleground appeared first on e27.