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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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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.

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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.

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