Five years ago, companies were looking for a strong candidate with deep specialisation and years of experience working within established systems. Today, especially in AI-adjacent policy, research, and innovation work, I find myself looking for a very different kind of person: someone who can learn in public, stay humble, adapt quickly, and think across disciplines without becoming intellectually shallow.

We no longer look only for specialists who know one chapter extremely well. We look for people who can read the whole book. In our space, that means navigating technology, policy, communication, ethics, and human behaviour simultaneously.

The shift became clear to us during a recent hiring discussion for a project involving AI governance and regional policy engagement. We discussed that if two candidates applied at the same time, who would we want to choose? One candidate had an exceptional résumé and prestigious credentials but struggled to adapt when project requirements changed continuously. Another candidate had fewer formal achievements but quickly integrated AI tools, synthesised policy information across disciplines, and independently proposed workable solutions. Increasingly, organisations, including us, are choosing the second profile. This isn’t an isolated hiring anomaly. It mirrors a massive global shift.

According to the 2026 PwC Global AI Jobs Barometer, skills required for AI-exposed roles are evolving 66 per cent faster than those in non-AI roles, pushing organisations to rethink hiring metrics beyond static credentials.

Today, we look for people with strong soft skills, consistent judgment, and the ability to operate in resource-constrained environments. Experience under pressure often reveals whether someone can adapt, prioritise, and continue functioning effectively in uncertainty. Experience in using AI or AI automation has also become important. Looking back, only three years ago, AI proficiency was barely discussed in hiring conversations, illustrating how rapidly organisational expectations have shifted.

Also Read: Generalist or specialist? Building future-proof skills in the age of AI

When access to information becomes increasingly universal through AI, competitive advantage shifts away from memorisation and toward judgement, adaptability, communication, and the ability to navigate uncertainty.

What is happening is not only the arrival of AI, but also the transformation of the working environment, which now requires people with diverse capabilities. Forward-thinking institutions need individuals who are well-rounded and understand how to continuously develop within the framework of their roles. Undoubtedly, deep expertise remains valuable, but agile teams must combine that specialised knowledge with speed, adaptability, and cross-domain collaboration.

Many outcome-oriented organisations have started asking four important questions in hiring:

Can the employee interpret problems rather than simply execute instructions? Can the employee collaborate with AI critically without losing independent judgment? Can the employee think creatively across disciplines? Can the employee operate independently under uncertainty?

Traditionally, these questions were often initially answered through résumés or CVs combined with HR interviews. In some cases, organisations also use standardised testing systems to measure capabilities numerically. Today, however, many organisations are beginning to realise that traditional hiring signals alone may no longer accurately predict long-term adaptability in AI-driven environments.

Also Read: AI’s tipping point: Why 2026 will separate the leaders from the laggards in financial services

My advice is to learn how to work effectively with AI and see it as a colleague whose capabilities can complement your own. Always prove that the information generated by AI is accurate and not misleading. Make AI part of your work and decision-making process because we place importance on evidence of real-world thinking through interdisciplinary collaboration, problem-solving principles, and the ability to manage uncertainty in AI-generated information. At the same time, organisations must be careful not to confuse AI-assisted speed with genuine understanding or good judgement.

All of this constantly makes me think that the concept of “great talent” is changing and spreading across industries. In other words, every industry increasingly agrees that people with great talent are those who possess fundamental qualities such as adaptability, learning ability, communication skills, and decision-making capability. In the future, each of these qualities will become separate skills that require even deeper mastery. More importantly, these skills must be visibly demonstrated during real work situations.

One challenge many organisations are currently facing is that many still prioritise stability and predictability, which conflicts with the rapidly changing nature of today’s world. At the same time, there is also a risk that organisations may begin undervaluing deep expertise in favour of constant adaptability. The challenge is not replacing expertise, but combining expertise with the ability to evolve continuously alongside AI.

Some employees who succeeded under older models of work may struggle to adapt if they rely solely on established expertise without integrating AI into their workflows. This contrasts with the new generation of great talent, who are able to adapt to changing environments by working alongside AI.

The future workforce may not be divided between technical and non-technical workers, but between those who can continuously learn alongside AI and those who cannot. In that environment, great talent is no longer defined only by what someone knows, but by how quickly they can reinterpret, apply, and evolve that knowledge in changing conditions.

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Also Read: Oil crashed 5% but Bitcoin jumped US$4K, altcoins surged 2X harder: What’s driving this?

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There is a moment, somewhere in the lifecycle of every major consumer platform, when the marketing playbook breaks. Banner ads stop working. Sponsored posts lose their edge. The audience, which has grown up inside the platform, develops an immunity to anything that feels like a paid placement.

In Southeast Asian gaming, that moment has already passed, and most brands are still running the old plays.

Also Read: The mobile-first myth that is costing SEA’s gaming industry billions

A gaming report by Southeast Asian gaming marketing agency Ampverse puts the structural shift in unambiguous terms: “Creators are not media placements; they are gatekeepers of trust.” In Southeast Asia, the report notes, a single creator can define how a game is perceived, and long-term creator relationships consistently outperform short-term influencer buys.

More than 50 per cent of gamers in the region regularly watch gaming content, and discovery — the moment a potential player first encounters a new game — increasingly happens through creators rather than app store rankings or paid advertising.

That is not a marginal shift. It is a fundamental restructuring of the distribution stack.

Why the influencer playbook fails in gaming

To understand why most brand campaigns in Southeast Asian gaming underperform, it’s helpful to examine how gaming creators differ from conventional social media influencers.

A lifestyle influencer operates on reach and aesthetic. Their audience follows them for a curated version of a life — the products they use, the places they visit, and the image they project. The relationship between influencer and follower is aspirational but relatively thin. A sponsored post slots neatly into that framework because the influencer’s identity is already partly commercial.

Gaming creators operate on trust and competence. Their audience follows them because they are genuinely good at games, genuinely entertaining to watch, and genuinely part of the same community. When a gaming creator endorses a title, their credibility is on the line in a way that a lifestyle influencer’s rarely is. Gamers can tell immediately whether a creator has actually played a game or is simply reading a script. The community does not forgive inauthenticity, and it does not forget it.

The Ampverse report captures this dynamic precisely: “Gaming audiences reward brands that participate meaningfully.” The word “meaningfully” is doing significant work in that sentence. It is not enough to pay a creator to post. Brands that win in this environment are those that enter through creators and communities, build long-term presence, create value rather than noise, and respect gaming culture on its own terms.

The creator economy inside gaming is structurally different

Southeast Asia’s gaming creator ecosystem has several features that distinguish it from both Western gaming markets and the broader regional creator economy.

Also Read: Gaming in SEA: Understanding the growing opportunity for SMEs and payment providers

First, community density. Discord servers, Facebook Groups, in-game guilds, and live tournament formats form the connective tissue of gaming communities across the region. The Ampverse report describes these structures as “the backbone of long-term engagement” and argues that successful brands and publishers treat communities as assets rather than audiences. This is not a metaphor; it reflects the reality that in markets like the Philippines, where the report describes a “highly social gaming culture” with games spreading “virally through creators and peer networks,” community infrastructure is the actual distribution mechanism.

Second, the primacy of live formats. Creator-led tournaments and live events consistently outperform static campaigns in Southeast Asia, delivering high watch time, repeat engagement, and organic social amplification. The report’s summary is pithy and correct: “In Southeast Asia, participation beats exposure.” A campaign that invites players to do something — compete, collaborate, contribute — will always outperform one that asks them to watch and click.

Third, the speed of creator-to-commerce crossover. The Ampverse report identifies an emerging trend that has significant commercial implications: gaming creators are increasingly launching mainstream consumer products. This is not peripheral to the gaming economy; it is evidence of how deeply gaming creators are embedded in their communities’ consumption behaviour. A gaming creator who launches a beverage, a clothing line, or a peripheral product is not diversifying away from gaming; they are monetising the trust they have built inside it.

The startup opportunity hiding in plain sight

The gap between what brands need and what the current market provides is, in startup terms, a problem worth solving. Most brands entering Southeast Asian gaming markets lack three things: the contextual knowledge to identify which creators are genuinely influential versus merely large, the infrastructure to manage long-term creator relationships at scale, and the measurement frameworks to evaluate performance beyond impressions and reach.

All three are addressable by technology. Creator intelligence platforms that map gaming community influence rather than follower count, relationship management tools designed for the cadence and format of gaming partnerships, and attribution models that account for community-driven conversion rather than last-click metrics; these are the products that the next wave of gaming-adjacent startups in Southeast Asia will be built around.

The Ampverse report notes that many global strategies fail because they are “copied from Western playbooks.” That observation extends to the creator strategy. Western influencer marketing infrastructure, which was largely built for Instagram and YouTube at a time when reach was the dominant metric, is a poor fit for a region where participation, community, and long-term trust are the actual levers of commercial performance.

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

Brands that are still buying short-term influencer slots in Southeast Asian gaming are not just leaving money on the table. They are actively building a reputation for inauthenticity in communities that have long memories and loud voices. The creators who matter in this market are not waiting for brands to catch up; they are already building the next generation of distribution infrastructure without them.

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Most hyper-growth businesses are not solving a marketing problem. They are wrestling with scaling pains masquerading as one.

Here is the truth: the playbook that powered your first million in sales will sabotage you at ten million. What flourished amid disorder now overwhelms. Once agile teams falter in the tumult, while executives, previously decisive, become mired in deliberations over processes, coordination, and infrastructure, imperatives that emerged only with scale.

This reflects not a marketing deficit, but a transitional oversight.

Two marketing realms collide

Having led corporate marketing campaigns and mentored early-stage startups, I have witnessed two starkly opposing playbooks.

Enterprise marketing prioritises methodology, alignment, and scalability. It is rigorous, data-centric, and optimised for sustained expansion. Ample budgets support specialised teams, with every initiative rigorously evaluated for reproducibility. The emphasis extends beyond outcomes to their consistent replication.

In contrast, startup marketing embodies resourcefulness and velocity, fixated on proximate impact. Perfection yields to pragmatism; traction reigns supreme.

The critical error lies in presuming seamless transferability between these domains.

A fast-scaling firm transcends startup volatility yet falls short of enterprise maturity. It inhabits an interstitial phase where legacy approaches obsolesce, and nascent frameworks remain undefined.

The scaling pitfall: Where growth grinds to a halt

Misaligning marketing orientation yields dual pitfalls. Prematurely appointing an enterprise marketer begets premature systematisation for nonexistent challenges. Meetings proliferate; velocity diminishes. The nimble operation transforms into a ponderous vessel.

Also Read: The secret weapon of marketing? Why every business needs a CDP

Conversely, retaining a startup-oriented marketer indefinitely erodes efficacy. Proven campaigns wane; redundancy mounts. Growth plateaus, not from market exhaustion, but methodological limits.

Tactics themselves are not at fault.

Unlocking marketing at scale

The ideal marketing executive possesses these attributes:

• Constructs scalable processes devoid of bureaucratic excess, with discretion to adapt.
• Advances expeditiously while anchoring to strategic imperatives.
• Validates hypotheses through empirical analysis, adept in quantitative and qualitative realms.
• Excels amid constraints, leveraging them for ingenuity.

Such leaders are rare, yet instrumental in distinguishing plateaued companies from those achieving breakout velocity.

Spotting if you are snagged

Thriving organisations discern this inflection and recalibrate their marketing apparatus accordingly. I have watched too many high-potential ventures sputter by ignoring this evolution.

If your marketing efforts are falling short, use these simple checks to pinpoint the issue:

• Are you stuck in startup mode? Look for one-off campaigns with no repeatable blueprint, a knee-jerk rejection of any process, and constant crisis firefighting. These signs show you are clinging to early-stage habits that no longer fit your scale.
• Are you acting too corporate too soon? Watch for endless meetings, decisions stalled by alignment talks, and complex systems built for problems that have not arrived yet. This flips the script, putting structure ahead of speed.

These questions reveal exactly what to adjust.

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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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Not long ago, turning an idea into something tangible required time, technical resources, and often a fair amount of patience.

Ideas waited.

They sat in notebooks, Slack threads, or development backlogs while founders debated feasibility, budgets, and timelines. Before anything could be tested, it typically needed approvals, specifications, and someone technical to bring it to life.

Recently, a late-night conversation reminded me just how much that assumption has changed.

The conversation that changed the question

It started with a WhatsApp exchange with entrepreneur and strategist Vicky Vaswani.

He had shared a book and pointed out something he found interesting – not the content itself, but a small interactive feature within the reading experience.

At first, I did not even understand what he meant.

I was looking at the landing page while he was referring to the book interface itself.

Then came the clarification.

The book was uploaded as a PDF, and almost jokingly, he mentioned that Seraphina – my AI twin – could probably summarise it.

Minutes later, the summary was done.

That was the easy part.

What caught my attention, however, was not the summary.

It was the interaction.

A linked chapter structure. A smoother mobile reading flow. Something that made static content feel more immersive and easier to navigate.

And almost instinctively, I replied: “I can take any PDF and make it into a digital flip page.”

Not as a polished offer.

Not as a planned product roadmap.

Just an observation.

Then came the question every founder eventually hears: “Do you have a sample?”

Historically, this is where momentum often slows.

You explain. You promise.

You say you will revert after checking with a developer or technical team.

You sell the idea through imagination.

Instead, I opened Lovable and started building.

Roughly 15 minutes later, there was a working proof of concept.

It was not formally launched. It was not meant to be perfect.

It was simply my interpretation of the idea Vicky had described – a digital reading experience paired with AI-generated summaries designed to make long-form content easier to consume.

His response was immediate.

And while the prototype itself was interesting, I quickly realised something more important:

The build was not the story.

What happened next was.

Also Read: Beyond the buzz: How AI and sustainability are reshaping design, manufacturing, and construction in APAC

The prototype that closed the deal

The following day, I was on a call with an existing client whose website I was helping develop.

During the conversation, I showed her the concept.

This was not something she had originally requested.

It was simply a proof of possibility.

She saw it. Liked it.

And chose to implement it immediately as an additional feature.

The top-up happened shortly after.

The commercial value itself is not the headline here. In fact, the amount reflected speed and optimisation more than the true value of the capability.

What mattered was the sequence.

A conversation sparked an idea.

An idea became a prototype.

The prototype changed the sales conversation.

And the sales conversation became revenue.

That progression would have looked very different even a few years ago.

AI is not changing how founders build, it is changing how founders sell

This is why I believe AI is not merely changing how founders build.

It is changing how founders sell.

Historically, entrepreneurs pitched possibilities.

They relied on decks, descriptions, and imagination.

Customers were often asked to visualise outcomes before they existed.

Today, AI-assisted tools are closing that gap.

Instead of saying, “Imagine if this worked like this.” Founders can increasingly say: “Here – try it.”

That shift matters.

Buyers rarely hesitate due to a lack of interest alone.

More often, they hesitate because of uncertainty.

They cannot visualise the outcome.

They fear making the wrong decision.

They struggle to bridge the gap between concept and lived experience.

A working prototype reduces that friction.

Not because it guarantees success, but because it transforms abstraction into something tangible.

Cheaper experimentation, compressed timelines

In many ways, AI has made experimentation dramatically cheaper.

And that changes the economics of entrepreneurship.

I have seen similar patterns emerging beyond my own projects.

In recent Money and AI Launchpad sessions, participants – many without traditional technical backgrounds – moved from ideas to live micro-SaaS applications within just 2.5 days. Alongside the build itself, they developed marketing visuals and promotional copy to support their launches.

Also Read: How sailing as a teenager prepared me for a career in tech and gaming

The real shift was not simply faster development.

It was compressed experimentation.

Ideas no longer needed months of commitment before validation could begin.

They could be tested while momentum was still alive.

Not replacement, leverage

This is perhaps where AI discussions often become misunderstood.

Much of the public conversation still revolves around replacement.

Will AI take jobs? Will it remove the need for people?

My experience has been different.

AI has not removed the need for judgment, taste, or strategy.

If anything, those skills matter more.

Execution, however, has become significantly cheaper.

Through my own workflows, supported by Seraphina and a growing ecosystem of AI tools alongside platforms like Lovable and systems we have long explored through People’s Inc. 360, I increasingly see AI functioning less as novelty and more as infrastructure.

And honestly?

I would have burned out doing this manually.

Not just the thinking. The execution.

Managing multiple ideas, testing concepts, supporting communities, refining workflows, and building across several initiatives simultaneously would have been unsustainable without AI-assisted execution.

This is why I often describe AI not as a replacement, but as leverage.

The founders benefiting most from this shift may not necessarily be those with the largest teams or deepest technical expertise.

Increasingly, they may be the ones who learn how to prototype quickly enough to test ideas before momentum fades.

AI did not eliminate the importance of good ideas.

Nor did it eliminate the need for human insight.

But it did make prototyping cheaper.

And in doing so, it may have quietly changed how modern entrepreneurship works.

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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 post AI did not change how founders build, it changed how they sell appeared first on e27.

Southeast Asia is one of the most exciting regions in the world for founders. With a fast-growing middle class, accelerating digital adoption, and a wave of ambitious entrepreneurs building across borders, the energy is undeniable. And yet, for every startup that cracks the APAC market, there are many more that struggle.

The difference usually isn’t the product. It isn’t the funding. It’s the execution.

It’s a gap that Jenga Anderson Global was built to close. Founded by Iris Xu, the firm works with growth-stage startups and fast-moving companies navigating the complexity of scaling across Southeast Asia and the wider Asia-Pacific region.

Why APAC is harder than it looks

Southeast Asia is not one market. It’s ten countries, hundreds of languages and dialects, and a dizzying mix of regulatory environments, payment preferences, cultural norms, and consumer behaviors. What works in Singapore doesn’t automatically work in Indonesia. What flies in the Philippines can fall flat in Vietnam.

According to Iris, the most common mistake founders make is treating APAC as a single block and building one strategy for all of it. The second is moving too fast before validating product-market fit in even one APAC country.

“Founders often think APAC expansion is about moving fast. It is, but only if you move in the right order,” says Iris Xu, founder of Jenga Anderson Global. “Too many teams copy their Singapore playbook into Indonesia, Vietnam, or the Philippines without first testing payment habits, trust signals, local partners, and regulatory assumptions.”

The other pattern she sees repeatedly is overbuilding too early. Teams set up entities, hire local staff, and enter arrangements before the operating model is clear. That creates avoidable cost and restructuring further down the road. “In APAC, the winners are not just the fastest movers. They are the founders who sequence well,” she adds.

The founder behind Jenga Anderson Global

Before starting Jenga Anderson Global, Iris built her career across private equity and consulting, focusing on growth, investment, structuring, and cross-border business strategy. She also led technology, media, and telecoms investment efforts under a multi-family office, work that put her in close proximity to founders, investors, and fast-moving technology businesses across the region.

That background shaped a particular way of thinking about expansion. “Growth is not just about capital or market opportunity,” Iris says. “It’s about turning ambition into an executable structure with the right jurisdiction, governance, banking, hiring, tax, and compliance foundations in place.”

The idea for Jenga Anderson Global crystallized from seeing the same gap play out repeatedly: founders with strong businesses who struggled with the practical execution of expanding across jurisdictions. They needed more than a service provider to file documents. They needed someone who could help them think through structure, compliance, banking, and hiring in the right order.

The firm’s name is deliberate. “Jenga reflects how I think about building a business,” Iris explains. “It is about using limited pieces efficiently to build the highest possible tower. Every piece matters, and sequencing matters. Sometimes the tower may fall, but in business, as in the game, you can always learn, rebuild, and start again, ideally with better structure and judgment each time.”

Also Read : Top 3 popular GEO monitoring tool for SEO optimisation targeting service industry in Singapore

The APAC ecosystem right now

Despite global headwinds, Southeast Asia continues to attract serious founder and investor attention. The fundamentals are strong: a young, digital-native population, rising consumer spending, and a startup ecosystem that is maturing fast, with more local talent, more local capital, and more locally-grown success stories than at any previous point.

What’s also shifting is how founders are building. Iris sees a generation of APAC companies that are regional from day one. Rather than thinking about one domestic market first and international expansion later, they are designing their companies, teams, payment flows, and investor story with cross-border growth already built in.

AI is accelerating the timeline. Smaller teams are moving faster, serving more markets, and automating operations that once required much larger headcount. But Iris thinks founders may be underestimating what that speed demands structurally. “As companies become more AI-enabled and cross-border, questions around data, tax, employment, licensing, payments, and governance become more important, not less,” she says. “The opportunity in APAC is very real. But the winners will be founders who combine speed with discipline: strong product, clear market sequencing, and a structure that can actually support regional scale.”

How Jenga Anderson Global helps startups scale

Jenga Anderson Global works with growth-stage startups and fast-moving companies that are serious about expanding in Southeast Asia and beyond. The firm helps founders use Singapore as a base to establish, operate, and scale across the region, supporting them across corporate structuring, governance, compliance, tax and accounting coordination, HR and work pass solutions, banking readiness, and ongoing operational execution.

The approach goes beyond strategy. “What clients value most is that we don’t just give advice from a distance,” Iris says. “We help them connect strategy with execution, turning expansion plans into the right structure, process, and trusted local support on the ground.”

Also read : Ecosystem Roundup: Digital on the surface, cash underneath

Case study: From strategy to scale

One client was a fast-growing technology company using Singapore as its international base. They had strong investor interest, but their corporate structure wasn’t ready for cross-border growth. Jenga Anderson Global helped align incorporation, governance, banking readiness, hiring, work passes, tax and accounting coordination, and future fundraising considerations into one practical roadmap. The result was a structure that could support real global expansion, not just a paper presence.

What separates the ones who make it

After working with founders across different markets, industries, and growth stages, Iris has identified a few things that consistently set successful APAC expansions apart.

First: intellectual humility. The founders who do well are the ones who walk in curious, not convinced. They ask questions before they make decisions. They hire locally, listen locally, and adapt quickly.

Second: execution discipline. APAC rewards founders who can move fast and stay organized: clean financial setup, clear accountability, and systems that can scale.

Third: the right partners. Founders don’t have to figure out Southeast Asia alone. The ones who scale fastest find the right people early: advisors, operators, and local hires who have already navigated the terrain.

On what ultimately separates those who make it from those who don’t, Iris is direct: “The biggest difference is not just speed. It is learning speed. The founders who succeed in APAC move fast, but they also listen fast, adapt fast, and correct course fast. They don’t assume one playbook will work across every market. They stay close to customers, local teams, regulators, banks, and partners, and they build enough structure around the business so that speed doesn’t turn into chaos.”

Meet Jenga Anderson Global at Echelon Singapore 2026

Jenga Anderson Global will be exhibiting at Echelon Singapore 2026 at Booths M14 and M15. For founders thinking about expanding into Southeast Asia, or those already in the thick of it, it’s a chance to have a real conversation with a team that has seen the full picture.

Visitors to the booth can expect a practical expansion conversation covering market-entry sequencing, structuring, compliance, banking readiness, hiring, and local execution. Jenga Anderson Global will also be sharing a market-entry checklist to help founders assess what to prepare before expanding through Singapore or into other APAC markets.

Southeast Asia remains one of the biggest opportunities in the world for ambitious founders. The question isn’t whether to be here. It’s whether you’re set up to win.

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Featured Image Credit: Jenga Anderson Global

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Southeast Asia’s gaming industry loves a headline number. And the headline number everyone reaches for is this: mobile accounts for roughly 70 per cent of the region’s total gaming revenue.

It is a clean, compelling statistic that has become the intellectual shorthand for an entire investment and market-entry thesis. Build a mobile game, localise it loosely, run some ads, and tap into a population of nearly 290 million gamers. Job done.

Also Read: Gaming in SEA: Understanding the growing opportunity for SMEs and payment providers

Except it is not that simple. A closer reading of a gaming report by Southeast Asian gaming marketing agency Ampverse reveals a market that is simultaneously larger and harder to monetise than the headline suggests, and one that is generating billions of dollars in downloads while leaving significant revenue on the table.

The scale-monetisation gap is real, and it is widening

In 2025, Southeast Asia’s gaming market generated approximately US$6.6 billion in revenue, growing at roughly 9 per cent year on year. Mobile gaming alone is projected to generate approximately US$4.8 billion by 2028, with PC and download games contributing a further US$1.5 billion. The broader ecosystem, incorporating advertising, creators, esports, and live services, could reach US$14 billion by 2030.

Those are extraordinary numbers. But here is the problem: Southeast Asia also ranked among the top two regions globally for mobile game downloads, recording nearly two billion installs in a single quarter. Two billion installs. And yet the region’s revenue does not come close to matching that install velocity in proportional terms.

The disconnect comes down to average revenue per user (ARPU). Across most of Southeast Asia’s six core gaming markets (Indonesia, the Philippines, Thailand, Vietnam, Malaysia, and Singapore), ARPU remains structurally low. The exception is Singapore, which has the smallest gamer base in the region (approximately four million) but the highest ARPU of any market. Singapore functions less as a consumer gaming market and more as a regional headquarters for publishers and platforms making bets on the rest of the region.

Vietnam offers perhaps the starkest illustration of the gap. With 55 million gamers, it is the second-largest market by player count, behind only Indonesia. The Ampverse report describes Vietnam as “price-sensitive but highly engaged”, a combination that is catnip for install metrics and a persistent headache for monetisation teams.

Players in Vietnam are deeply invested in their games; they are simply not converting into paying users at the rates publishers need to justify the cost of acquisition.

Free-to-play is not a monetisation strategy; it is a starting point

The dominance of free-to-play models in Southeast Asia is often cited as evidence of the region’s accessibility. That is true. But free-to-play also creates a structural ceiling on revenue that publishers and startups are only now beginning to dismantle seriously.

Also Read: How a US$14.8B SEA gaming market is turning tournaments into media ecosystems

The Ampverse report notes that gaming revenue is “expanding beyond traditional in-app purchases into content, communities, and brand ecosystems.” That is a significant shift. It signals that the primary monetisation lever for the next phase of Southeast Asian gaming growth is not in-app purchases; it is the broader economic activity surrounding the game itself.

This includes livestreaming revenue, creator-driven commerce, tournament prize pools and sponsorship, branded in-game activations, and the emerging space of user-generated content (UGC) that blurs the line between player and producer.

In markets like Thailand, which the report describes as one of Southeast Asia’s most monetised gaming markets with strong e-sports infrastructure and high acceptance of premium brand activations, this ecosystem-level monetisation is already more advanced than in neighbouring markets.

The platform story is more nuanced than mobile vs everything else

It would be a mistake to read the mobile dominance numbers as evidence that PC and console are irrelevant. The Ampverse report notes that the console remains niche but is growing in affluent urban centres across the region. More importantly, many of Southeast Asia’s most engaged gamers are not single-platform users; they move fluidly between mobile and PC depending on the game, the time of day, and the social context.

For startups building gaming-adjacent businesses (infrastructure tools, analytics platforms, creator monetisation products, and social layers), this cross-platform behaviour is commercially significant. A player who starts a game on mobile during their commute and continues on PC at home is a different kind of user than the pure mobile demographic that install-volume figures suggest dominates the region.

What the data actually tells investors and founders

For investors evaluating gaming or gaming-adjacent opportunities in Southeast Asia, Ampverse’s data points to a market in the middle of a structural transition, from a downloads-and-installs economy to a retention-and-monetisation one. The startups most likely to win in this environment are not those chasing install volume, but those building the infrastructure that converts engagement into durable revenue.

That means community platforms, creator monetisation tools, live event technology, regional analytics products, and brand-to-gaming partnership intermediaries. The US$14 billion 2030 projection is not a passive forecast; it is a roadmap of the commercial infrastructure that needs to be built to make it real.

Also Read: AI in gaming: How Southeast Asia became the testing ground for virtual companions

The mobile-first thesis is not wrong. It is just incomplete. Southeast Asia’s gaming economy is mobile by default and complex by nature, and the entrepreneurs who understand the difference between those two things are the ones who will build the companies worth watching.

The post The mobile-first myth that is costing SEA’s gaming industry billions appeared first on e27.

At Echelon Philippines 2025, Carlo Almendral, CEO and Co-Founder of AIFirst, delivered a compelling keynote speech on the second day of the event, painting an optimistic picture of the Philippines’ place in the rapidly evolving AI landscape.

Drawing on the country’s unique strengths, Almendral highlighted the immense potential the Philippines holds in embracing and advancing artificial intelligence. He outlined how strategic implementation of AI technology could unlock transformative opportunities across key industries, positioning the nation as a competitive player on the global stage.

His address served as both an inspiration and a call to action for Filipino innovators and business leaders alike.

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For decades, the architecture of international education ran in one direction. Asian families sent their children west, and the financial plumbing followed. Tuition flowed out of emerging markets into the bank accounts of universities in London, Boston, Sydney, and Toronto. The infrastructure was built around that single assumption.

It no longer holds. Students across Asia are increasingly choosing to study closer to home. Singapore is drawing record interest from South Asian applicants. Hong Kong’s universities, openly naming Singapore as a rival, are moving to lift their non-local enrolment ceiling to 50 per cent of local places. Malaysia, Vietnam, South Korea and Japan are all positioning themselves as destinations rather than only sources. None of this is a temporary post-pandemic blip. The pivot east is structural, not seasonal, and it is moving faster than the headlines.

Asia is no longer just a source of international students. It is rapidly becoming a destination for its own.

The payment rails carrying tuition between Asian families and Asian universities were not built for any of this.

The scale and the shift

The numbers are not small. India alone sends over a million students abroad each year. China has a similarly large outbound population, spread across more than 80 countries. Southeast Asia adds another 350,000-plus, making it one of the fastest-growing source regions globally. HolonIQ values the international education market at US$196 billion annually, projected to reach US$433 billion by 2030.

For most of these families, funding an overseas education is the largest cross-border transaction of their lives. Tuition, accommodation deposits and administrative fees get bundled into a handful of high-value transfers, executed under tight timelines and complicated regulation. Increasingly, both ends of that flow sit inside Asia. The infrastructure carrying it does not.

A system optimised for the wrong thing

The payment stage is where an offer-holder becomes an enrolled student. A failed transaction here is a lost enrolment. As a result, most institutions and payment providers optimise for a single outcome: whether the transaction goes through.

That is the wrong test. A payment going through is necessary; it is nowhere near sufficient. Conversion is the floor. It should not be mistaken for the ceiling.

Also Read: SEA’s SMEs aren’t lazy, but their payments infrastructure is

What families actually need is something harder to measure. They need to feel confident throughout. When a household is moving the largest sum of money it has ever moved, often for the first time, into a foreign banking system on a deadline tied to a visa, the absence of confidence is itself a failure mode, even if the money eventually arrives.

Asia’s payment paradox

The gap is particularly striking in Asia, home to some of the world’s most advanced domestic payment ecosystems. India’s Unified Payments Interface processed a record 21.6 billion transactions in December 2025 alone. Alipay and WeChat Pay have made cash close to optional across urban China. QR-based mobile wallets have rewired everyday commerce across Southeast Asia in under a decade.

Almost none of this translates across borders.

A Vietnamese family paying tuition to a campus in Singapore, or an Indian family wiring fees to Japan, is moving money between two digitally sophisticated payment economies, and still falling back on slow, opaque bank rails. Transfers route through multiple intermediaries. Foreign exchange costs are difficult to understand. Visibility into when funds will arrive is limited. The result is a fragmented experience layered on top of otherwise advanced financial systems.

Where friction becomes risk

For most families, this is not a transaction. It is a commitment, often the largest single transfer they will ever authorise, tied to a child’s future and a deadline they cannot move. And it is happening at a moment when households across the region are scrutinising every outgoing dollar.

The Chinese economy is in its longest stretch of consumer caution in two decades. Indian families are weighing rupee depreciation against rising overseas tuition. Across Southeast Asia, the post-pandemic squeeze has not fully lifted. In that context, an opaque foreign exchange margin is not a minor cost. An unexplained two-day delay is not a minor inconvenience. These are the moments where a family begins to wonder if they made the right choice. Some end worse: a missed enrolment deadline, or a switch to an informal channel that is faster but less safe.

Also Read: Digital payments: Adapting to a changing world

The institutional side wears the same friction differently. Tuition is high-value and compliance-sensitive. Every payment has to be reconciled against a student record, audited, and often chased through three or four intermediaries before it can be matched. Finance teams burn capacity on exception handling that legacy infrastructure was never designed to absorb at this scale.

A business-model problem dressed up as a technology one

Yes, capital controls and FX limits shape these flows. Mainland Chinese students operate under a US$50,000 annual personal quota. India’s remittance regime carries its own reporting obligations. Most emerging-market currencies come with constraints of some kind.

But these have always existed, and they do not explain the friction. The infrastructure to move money across borders, within these rules, already exists. What has been missing is any commercial reason to design it around the user, around a family in Surabaya wiring US$30,000 to Singapore, rather than around a global bank’s correspondent relationship in New York.

The challenge is less about whether payments can be completed and more about how they are experienced. Can the sender see where the money is at each stage? Are the fees and exchange rates legible? Does the confirmation arrive in time to matter? These are not edge cases. They are what trust is made of.

The metric no one is tracking

Payment success rates show up in every dashboard. Confidence does not. Yet confidence is what families remember.

A student who pays successfully but spends three days uncertain whether the money has arrived carries away a different impression of the institution than one who experiences clarity throughout. Across Asia, where decisions about where to study are shaped by agents, family WhatsApp groups and word of mouth more than by rankings or marketing, those impressions travel. Quickly, and across borders.

Also Read: SEA’s digital payments boom has a dirty secret: SMEs still run on cash

A market the incumbents were not built for

Cross-border education payments are a strange overlap: high-value, recurring, family-led, compliance-heavy, and increasingly intra-Asian. The legacy players solved a different version of this problem, one that ran from emerging markets to Western universities, in a world that no longer exists. The space is still shaped by their assumptions even as the flows move away from them.

Improving outcomes from here is not about adding more payment methods to the existing stack. It is about rethinking what visibility, trust and usability look like when both ends of the transaction sit inside Asia.

Raising the standard

As Asia’s role in global student mobility expands, the standard for the payment systems underneath it has to expand with it. A transaction that completes is no longer enough. The question worth asking is whether the system delivers confidence to the family on the other end.

A simple audit, for any institution or fintech reading this: at every stage of an inbound education payment, what does the family actually see, and what do they not?

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For the past few years, the public image of artificial intelligence has been shaped almost entirely by the chatbot. People type questions; AI answers. People ask for summaries, emails, scripts, ideas; AI produces words.

This is genuinely powerful. But it is prologue.

The real future of AI is not conversation. It is action, and the distance between those two things is wider than most people appreciate.

LLMs are powerful, but they are mostly passive

Large Language Models are extraordinary instruments of language. They explain, translate, summarise, code, and communicate with a fluency that would have seemed miraculous a decade ago. But for all their sophistication, most LLMs are fundamentally passive.

They wait for instructions. They respond to prompts. They produce possibilities. They do not naturally perceive the physical world, test hypotheses in real environments, or improve through direct consequence the way humans and animals do.

Consider the difference in kind, not just degree:

A chatbot can tell you how to run a marketing campaign. An action-based AI system can launch the campaign, monitor performance, adjust targeting, swap creatives, reallocate budget, and learn which combination works best.

A chatbot can explain inventory management. An action-based AI system can do it, predict demand, place orders, negotiate with suppliers, detect shortfalls, and optimise warehouse movement in real time.

A chatbot gives advice. The next stage of AI executes.

That is the shift: from answering to acting.

Intelligence is not just knowing, it is doing

Human intelligence was never primarily linguistic. We do not become capable simply by reading books or holding conversations. We become capable by acting in the world and absorbing the consequences.

A child learns by touching, falling, adjusting, and trying again. A trader learns by watching how markets respond to their decisions. An athlete improves not through theory but through thousands of repetitions and the relentless feedback of performance. A driver becomes skilled not by memorising the highway code but by driving in actual traffic, with real stakes.

Real intelligence is grounded in consequence.

Also Read: Generalist or specialist? Building future-proof skills in the age of AI

This is precisely where LLMs hit a ceiling. Trained primarily on the accumulated record of human knowledge, they are exceptional at pattern recognition within language, but they do not automatically build a deep, grounded understanding of causality, physical reality, or long-term strategy. They know the map. They have not walked the terrain.

Action-based AI needs something different. It needs world models and reinforcement learning.

World models: An internal simulator for reality

A world model is an AI system that learns how the world works, not just how it is described. It learns cause and effect. It simulates possible futures. It can reason about what is likely to happen before committing to action.

This is categorically different from predicting the next word in a sentence.

A world model does not merely store the fact that dropped glasses break. It learns the relationships between objects, force, space, timing, and consequence. It can run mental simulations. It can ask: What happens if I do this? And explore the answer before anything has moved.

This matters enormously for real-world action. Before a robot moves through a space, it must model that space. Before a self-driving car changes lanes, it must model traffic. Before an AI agent manages a supply chain or deploys capital, it must understand how one action reshapes the situation it will face next.

This is the core problem that research teams are working on. Companies like QuantumAtlas.ai are building the reasoning infrastructure that sits beneath action, giving AI systems a structured, updatable picture of the world they are operating in.

If LLMs are AI’s voice, world models may become AI’s imagination.

Reinforcement learning: wisdom through consequence

Reinforcement learning trains AI not through data alone, but through experience, reward, feedback, trial, error, and iterative improvement.

This is how practical mastery actually develops. A salesperson improves after hundreds of client conversations. A portfolio manager sharpens instincts after watching markets respond to their choices. A product team learns which features matter after watching users encounter them in the wild.

Reinforcement learning gives AI a mechanism to improve based on outcomes rather than information. This is why it is indispensable for autonomous agents, robotics, logistics, financial optimisation, and any domain where the goal is not a correct answer but a better result.

An LLM can describe ten strategies. Reinforcement learning can test ten thousand, and discover which one actually works.

Also Read: Building the ASEAN AI archipelago: How Southeast Asia can secure its place in the global AI value chain

The next AI winners will be outcome companies

The first wave of generative AI built tools that help people get answers faster. That is genuinely useful. It is also, by itself, insufficient for a durable competitive advantage.

The next wave is organised around outcomes.

Businesses do not ultimately want more text. They want more revenue, lower costs, faster operations, better service, safer systems, smarter pricing, and higher productivity. They want results.

This is where action-based AI becomes qualitatively more valuable, and qualitatively harder to replace.

A company delivering an AI chatbot helps users save time. A company delivering an AI system that measurably improves revenue, reduces waste, or makes better decisions becomes embedded in the core of the business. The question shifts:

“What can this AI say?”

becomes

“What can this AI achieve?”

That is a much larger and much more defensible market.

Conversation will become the interface, not the product

None of this means conversation disappears. Natural language will remain one of the most important surfaces through which humans interact with AI. But it will become the interface, the doorway, not the destination.

Behind a simple instruction, AI will be connected to tools, data, software, sensors, robots, financial systems, supply chains, and business workflows. Consider what this actually looks like:

A user says, “Improve our customer response time.”

The AI does not offer suggestions. It analyses support tickets, identifies bottlenecks, rewrites response templates, routes urgent cases, monitors resolution time, and reports results.

A user says, “Find me the best investment opportunity.”

The AI does not explain asset classes. It scans data, models risk, simulates scenarios, monitors changes, and helps execute within defined parameters.

A user says, “Grow my marketplace.”

The AI does not produce a marketing plan. It identifies high-value sellers, optimises onboarding, personalises campaigns, monitors conversion, and improves retention.

The conversation is the instruction. The real value is everything that follows it.

Action requires memory, feedback, and responsibility

Moving from conversation to action demands more than capability. It demands accountability.

Action-based AI needs memory to understand past decisions and evolving context. It needs feedback loops to learn from what it did. It needs access to tools to execute, not just recommend. It needs safety controls to avoid acting blindly in high-stakes environments. And it needs a model of consequences to reason about risk before committing to a course of action.

Also Read: AI’s tipping point: Why 2026 will separate the leaders from the laggards in financial services

This is the architecture that matters most right now, and it is what separates serious infrastructure plays from surface-level AI wrappers. The most capable AI systems will not be chatbots. They will be intelligent operating systems for action, systems that perceive, reason, decide, execute, and learn.

The biggest opportunity is in the real economy

The largest AI opportunities are not in writing, image generation, or chat. They are in the industries that shape how the world actually functions: healthcare, education, logistics, construction, manufacturing, real estate, agriculture, finance, transportation, and government services.

These sectors do not need better conversation. They need better decisions and reliable execution.

A real estate AI should not only answer property questions. It should understand buyer intent, match listings intelligently, predict demand, support agents, manage leads, analyse pricing, and improve closing rates.

An automotive AI should not only describe vehicles. It should assess condition, predict resale value, recommend financing, detect fraud, and optimise dealership operations.

An e-commerce AI should not only write product descriptions. It should forecast demand, prevent fraudulent listings, improve delivery, recommend pricing, and build buyer trust.

These are not language problems. They are action problems. And solving action problems requires a grounded, dynamic model of the world, not just a fluent command of words about it.

From words to outcomes

LLMs changed the world by giving AI a voice, by making machine intelligence accessible to ordinary people in a form they could immediately use and understand.

But the next stage is larger.

The future of AI is not a machine that talks fluently. It is a machine that acts intelligently, one that can simulate reality, make decisions, test strategies, absorb feedback, and deliver measurable results in the world that matters.

The infrastructure for this future is being built now, quietly and carefully, by teams focused not on the next demo but on the next decade.

Conversation was the beginning. It opened the door.

Action is what happens when you walk through it.

The organisations, entrepreneurs, and investors who understand this shift earliest will hold a genuine and lasting advantage. The next AI revolution will not be won by whoever builds the most elegant chatbot. It will be won by whoever builds AI that can understand the world, act in the world, and make the world measurably better.

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