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.

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Singapore-based quantum-safe cybersecurity startup pQCee has raised US$3.9 million in a seed funding round, as governments and large enterprises begin moving post-quantum cryptography from research papers and standards discussions into procurement plans.

The round was co-led by SGInnovate and Lotus One Investment, with participation from In Group Holdings, Wavemaker Ventures, SUTD Venture Holdings, and Apsara Investments.

Also Read: How quantum computing moved from components to applications in 2024

This round follows a US$2.8 million institutional raise in 2022, which was co-led by Wavemaker Ventures and SEEDS, the investment arm linked to SG Growth Capital, with participation from SGInnovate, Mirana Capital, Paragon Capital Management, and Apsara Investments.

pQCee said the new capital will be used to expand its Singapore team, deepen its presence in Asia, and support market entry into the US, Europe and the Middle East.

The company sells post-quantum cryptography and key-management tools to organisations that need to protect sensitive data against the risk that encrypted information stolen today could be decrypted later once quantum computers become more capable. In other words, pQCee helps organisations protect their data from a future generation of quantum computers that could break today’s encryption.

In plain English: a hacker or state actor could steal encrypted data today, store it, and decrypt it years later when quantum computers become powerful enough. This threat, often called “harvest now, decrypt later”, has become a growing concern for banks, governments, telecom operators, and critical infrastructure providers. The risk is not that quantum computers can already break widely used public-key encryption at scale, but that adversaries can stockpile encrypted data now and wait for more powerful systems to emerge.

Standards are turning into deadlines

The timing is crucial here. In August 2024, the US National Institute of Standards and Technology finalised its first three post-quantum cryptography standards, including FIPS 203, which is based on the ML-KEM key-establishment algorithm. Those standards gave enterprises and vendors a clearer technical baseline after years of uncertainty.

MarketsandMarkets has estimated that the global post-quantum cryptography market will grow from US$302.5 million in 2024 to US$1.88 billion by 2029, a compound annual growth rate of 44.2 per cent. That forecast reflects both genuine concern and the reality that many large organisations have barely begun the work of discovering where vulnerable cryptography sits inside their systems.

The transition is likely to be slow. Cryptography is embedded in applications, networks, hardware security modules, identity systems, payment infrastructure and messaging platforms. For banks and public-sector agencies in Southeast Asia, the challenge is not only choosing new algorithms but replacing or upgrading legacy systems without breaking operational workflows.

pQCee’s products are aimed at that messy middle ground. Its flagship offering, SafeQuard, provides end-to-end encryption intended to reduce exposure to harvest-now-decrypt-later attacks. QKDLite is middleware for key management and is designed to work with standards including PKCS#11, ETSI QKD 014 and FIPS 203. The company also offers inoQulate for post-quantum public key infrastructure certificates and QuICScript, a browser-based tool that lets users experiment with a 20-qubit quantum simulator.

Also Read: Quantum computing market surges as companies shift focus to revenue: Report

Dr Teik Guan Tan, CEO of pQCee, said the company is focusing on practical deployment rather than abstract quantum risk.

“As global regulations tighten and the threat landscape evolves, organisations need practical, interoperable solutions they can adopt today,” he said.

A Singapore base for a cross-border problem

Although pQCee is looking beyond Southeast Asia, its Singapore base is significant. The city-state has positioned itself as a regional hub for quantum research, deeptech commercialisation, and cybersecurity regulation. Its role as a financial centre also makes it a natural early market for post-quantum security vendors.

Singapore has been building national quantum capabilities through programmes such as the National Quantum-Safe Network, while its banks, insurers and public agencies face rising expectations around resilience and third-party technology risk. Across Southeast Asia, regulators have taken a more active stance on cybersecurity, particularly in sectors such as finance, telecoms, energy and public services.

The region’s digital exposure is also increasing. Google, Temasek and Bain & Company estimated Southeast Asia’s digital economy gross merchandise value at US$263 billion in 2024. As more financial services, healthcare records, government services and enterprise workflows move online, long-lived sensitive data becomes more attractive to sophisticated attackers.

That gives quantum-safe security a regional logic, even if near-term enterprise spending remains selective. Many Southeast Asian organisations are still dealing with basic security gaps, ransomware, cloud misconfiguration and identity attacks. Post-quantum migration will compete for budget against those immediate threats. The vendors that succeed will need to show not only that quantum risk is real, but that migration can happen without excessive cost or disruption.

Competition is already global

pQCee enters a market that is technically specialised but increasingly crowded. Global players include UK-based PQShield, US companies SandboxAQ and QuSecure, and quantum communications firms such as Quantum Xchange. Large technology and security vendors, including IBM, Microsoft, Google, Thales, and Cloudflare, are also active in post-quantum standards, testing and deployment.

In Singapore, quantum communications company SpeQtral has focused on quantum key distribution and satellite-based secure communications. pQCee’s approach appears more centred on post-quantum cryptography, crypto-agility and enterprise integration, rather than selling quantum hardware as the core product.

The company has partnerships with Thales and Feitian for integration with hardware security modules and secure devices. It has also worked with Netrust and SendQuick to extend quantum-safe protection into digital identity, messaging and enterprise workflows, and with PQShield to align with post-quantum cryptographic standards. Other partners include Microsoft and TechCreate.

These partnerships matter because the post-quantum transition will not be won by point solutions alone. Enterprises will need tools that work with existing identity infrastructure, hardware security, cloud environments and compliance processes. Crypto-agility — the ability to swap or update cryptographic algorithms without rebuilding entire systems — is likely to become a key procurement criterion.

Paul Santos, co-founder and managing partner at Wavemaker Partners, said the migration burden will shape early demand.

“pQCee’s holistic suite of solutions simplifies post-quantum cryptography migration for enterprises, reducing complexity in integration, procurement, and cost,” he said.

Also Read: McKinsey: Strategic investment fuels Asia Pacific quantum computing expansion

The harder question is how quickly customers will move. Awareness has improved, but many boards still treat quantum risk as a future problem. Vendors such as pQCee must persuade buyers that migration planning should begin before cryptographically relevant quantum computers arrive, not after.

For Singapore, the bet is also strategic. Deeptech startups often struggle to move from research credibility to global commercial scale. pQCee’s new funding gives it more runway to attempt that transition. Whether it can convert standards momentum into recurring enterprise revenue will determine if it becomes another niche cybersecurity vendor or a meaningful player in the post-quantum infrastructure stack.

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Asia does not have a shortage of patents. It has a shortage of commercial pathways for technologies that sit unused inside large corporate portfolios.

That is the gap Piece Future is targeting through IPHatch, an open-innovation platform that gives startups access to patents from multinational companies and research institutions. Now entering its ninth year, IPHatch Asia 2026 is co-organised with the Hong Kong Trade Development Council and includes intellectual property (IP) from Panasonic, Murata, Nokia, CASIO, Ricoh and Nitto Denso, as well as universities including Tohoku, Nagoya and Hokkaido.

For Southeast Asian founders, the timing is relevant. Venture capital has become more selective, while startups face pressure to prove defensibility earlier. IPHatch’s pitch is that founders need not build every technology from scratch; they can use existing corporate IP as a base for new products.

Also Read: Set sail with intellectual property: Your business’s journey to success

e27 spoke with Jason Loh, founder of Piece Future, about how the model works and what it means for Asia’s startups.

The following interview has been edited for clarity and length.

Why do companies such as Panasonic, Nokia and CASIO choose to open patents through IPHatch rather than license them directly or keep them dormant?

Many multinational corporations invest heavily in R&D, and that leaves them with extensive patent portfolios. Some technologies are actively commercialised, but others may no longer fit the company’s core priorities, even though they still have commercial potential.

Keeping these patents dormant offers limited strategic value. Companies still bear maintenance costs without generating returns from those assets. IPHatch provides a proactive way to identify entrepreneurs who can find new markets or applications that the original patent owners may not have pursued internally.

Direct licensing also takes time, resources and market expertise. In many cases, large companies may not want to pursue opportunities outside their strategic focus, while startups that see the potential may not have the resources to access those technologies through conventional licensing channels.

Through IPHatch, startups take over responsibility for maintaining and commercialising the patents. For the IP originators, that can reduce costs while creating the possibility of licensing revenue, equity upside, partnerships and new commercial life for technologies that would otherwise remain unused.

You say winners receive “real IP ownership”. How does that work?

Winners receive outright ownership of the patent. Once matched, the IP is fully assigned to the startup, so they own it like any other company asset.

In exchange, startups provide an equity stake in their company, typically in the 5-10 per cent range, depending on how many patent portfolios they choose to take on. The more IP a startup wants to build on, the larger the stake.

With many applicants competing for a limited number of matches, what does IPHatch look for?

We evaluate startups against three criteria: the problem they are solving, the relevance of the IP, and the team’s ability to execute.

The strongest matches are those where the technology directly enables the solution and gives the startup a clear point of differentiation. We are not simply looking for interesting ideas. We want teams that can show why a specific patent is the right foundation for a particular problem, and how they plan to bring that solution to market.

Execution matters just as much. We look for founders who think commercially, stay grounded in real market needs, and can turn strong IP into a viable business.

How accessible is the programme for founders in markets, such as Vietnam, Indonesia, or the Philippines?

Founders in Vietnam, Indonesia, the Philippines and other Southeast Asian markets are very much part of this year’s and future cohorts. IPHatch does not require in-person presence to compete. Teams can pitch virtually instead of travelling to Hong Kong.

For localisation, we work with ecosystem partners, incubators and accelerators across Southeast Asia. That includes support for market access, introductions to local partners and customers, and access to facilities or co-working spaces where available.

Also Read: Unlock the secrets to IP success for your business

Many Southeast Asian founders join IPHatch when they are ready to expand beyond their home markets. At that point, we provide introductions, ecosystem connections and support to help them enter new markets and build strategic partnerships.

Once a startup is matched with a patent, what does the first year look like?

We do not impose a fixed timeline. Every startup has different product roadmaps and priorities. In many cases, the patent may only become relevant during phase two or phase three of MVP or product development.

Usually, the startup’s CTO or technical team reviews the patent in detail to determine how the underlying technology can be integrated into an existing product or used for a new one. Several of our startups have later filed new patents to protect enhancements or end-to-end solutions built on the original technology.

The original patent holders do not provide hands-on technical support. Piece Future runs technical translation workshops led by our IP engineers to help startups understand the patents and identify practical implementation opportunities. For startups that need direct technology transfer from inventors or patent owners, we run a separate programme called TechHatch.

How does the support structure differ from a typical three- to six-month accelerator?

We provide mentorship, market access, and IP strategy support. We work with governments, universities, incubators, and accelerators across more than 10 locations globally to help startups expand into new markets, build partnerships, and connect with customers, corporations and ecosystem players.

We do not provide direct funding, but we have a network of venture capital firms that follow our startups. We facilitate introductions when the company reaches the right stage and fits an investor’s thesis.

IPHatch is not a typical accelerator. Most accelerators focus on rapid validation, growth and investor readiness, usually ending with a demo day. IPHatch is a five-year IP commercialisation and venture-building platform. The support changes based on each startup’s business needs, technology maturity and growth trajectory.

Can you share an example of a dormant MNC patent becoming part of a commercial product?

Dresio is one example. It operates in musculoskeletal healthcare and uses computer vision to track body alignment and movement, helping clinicians make more objective assessments using data-driven insights and AI models.

Dresio was assigned patents from Nokia and Panasonic through IPHatch. The Nokia patent focuses on organising and retrieving dynamic content, allowing users to save, tag and search related data through intelligent markers. For Dresio, that supports the management and processing of large volumes of musculoskeletal images and movement data.

As Dresio expands into a broader wellness platform, it has also used Panasonic patents related to physiological information analysis. One patent describes a computer-based method that measures blood flow in multiple body parts and analyses the relationship between those measurements to estimate conditions such as stress, circulation, fatigue or overall health status.

Is the patent pool skewed towards hardware and deeptech, or can agritech and fintech founders also find a path in?

We have a growing portfolio of data, AI training, data management, and cybersecurity-related patents. These are among the most sought-after areas because they apply across multiple industries.

We are sector agnostic. We have agritech and fintech companies using patents in cybersecurity, data management, tracking and recognition technologies. Foundational technologies can be adapted across different sectors and use cases.

We also offer Portfolio X, which is designed for founders who do not want to pick from a fixed list. They can bring their business idea or problem, and we help match it against patents in our IP bank.

Is there a financial cost to founders, and what happens if the startup fails to commercialise the patent?

There is no fee to apply for or participate in IPHatch during the selection process. If a startup is matched with a patent, Piece Future works with the team to structure a commercial agreement based on the technology and business opportunity.

If a startup is unable to commercialise the IP, the outcome is governed by the terms of that agreement. The objective is to give founders the best opportunity to succeed while ensuring the IP continues to be managed responsibly.

Do you see this model changing how startups in Asia think about R&D?

One misconception is that every founder needs to invent a new technology to build a successful company.

Not every innovator is an inventor, and not every inventor becomes an innovator. Inventors create new technologies. Innovators create value by applying technologies to real-world problems.

Also Read: How to deter copycats and protect your brand value

There are thousands of patented technologies that represent years of R&D but remain underutilised because they no longer fit the patent holder’s current roadmap. Founders should ask not only, “What can I invent?” but also, “What valuable technology already exists, and how can I apply it to solve a real problem?”

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