The Philippines is carving out a distinctive path in Southeast Asia’s economic development by prioritising infrastructure reliability as a core productivity driver.

The “Philippine Private Capital Report 2026” by Foxmont Capital Partners highlights a key insight: while headline-grabbing mega-projects draw attention, it is the everyday dependability of utilities and services (power, water, internet, ports, and transport) that consistently unlocks firm-level productivity gains. That lesson is immediately relevant not only inside the Philippines but across fast-growing economies in Southeast Asia.

Reliability: the hidden productivity factor for firms

For many Philippine small and medium-sized enterprises (SMEs), the cost of unreliable infrastructure is a day-to-day reality. Frequent power interruptions, variable water pressure, and intermittent internet connectivity lead firms to “self-insure.”

Also Read: From agritech to AI ops: 15 startups driving Philippines’s innovation shift (Part 2)

Common responses include buying backup gensets (diesel generators), installing water storage and purification systems, and subscribing to multiple internet providers or deploying satellite/4G/5G failovers. These risk-mitigation costs are real and persistent: they reduce available cash flow for investment in productivity-enhancing technologies, staff training, or capacity expansion.

This dynamic plays out across the archipelago in ways that district-level statistics can obscure. In Metro Manila, rolling brownouts and overloaded distribution transformers are a drag on manufacturing and business process outsourcing (BPO) firms that require continuous operations.

In secondary cities—like Cebu, Davao, and Iloilo—infrastructure reliability can be the difference between attracting an export-oriented factory or losing a contract to a more predictable location in Vietnam or Thailand.

For agricultural processors in Mindanao, irregular electricity and logistics delays increase post-harvest losses and reduce competitiveness.

The takeaway is that policymakers should measure more than installed capacity or kilometres of new roads. Operational metrics—uptime, variance, frequency of service interruptions, repair response times—are the practical measurements that determine whether infrastructure actually supports business.

Regional value chain integration depends on predictability

Reliability matters not just for domestic operations but for the Philippines’s ability to link into regional value chains. ASEAN economies are competing to attract deeper segments of electronics, automotive, and high-value manufacturing supply chains. These sectors require predictable inputs, just-in-time delivery and stable utilities. A factory that must maintain its own generators to meet contract uptime clauses faces higher operating costs and lower margins than one located where the grid is consistently reliable.

The Philippines has a comparative advantage in skilled labour and an English-speaking workforce, making it attractive for higher-value services and certain light manufacturing. But to capture a larger share of electronics or automotive component manufacturing relocating from China, reliability gaps must be closed.

Southeast Asian neighbours such as Vietnam and Malaysia have made notable progress in grid stability and logistics efficiency in recent years; the Philippines’ unique geography—an archipelago of more than 7,000 islands—raises the bar for integrated solutions that combine national grid upgrades with localised microgrids, improved port handling and seamless digital logistics.

Policy progress—and persistent, localised challenges

There are encouraging developments. Administrative digitalisation—examples such as digital company registration via eSPARC—has lowered fixed costs and simplified formalisation for businesses, demonstrating how procedural digitisation can complement physical infrastructure reliability.

Also Read: Why 2025 is a milestone year for startup funding in the Philippines

Similarly, regulatory changes to telecommunications foreign ownership rules have been aimed at fostering competition, attracting more capital, and improving service quality. When combined with incentives for private investment through public-private partnerships (PPPs), these reforms can magnify the impact of physical infrastructure upgrades.

However, challenges remain. Geographical disparities in service quality are stark: urban centres may enjoy multiple fibre routes and near-continuous power, while remote islands rely on ageing diesel plants and maritime supply chains vulnerable to weather. Local government units (LGUs) display uneven digital adoption: some provinces streamline permits and provide online business services, while others still depend on paper-based processes that delay firm expansion.

Addressing these localised disparities requires a two-pronged strategy: strengthen national standards and oversight for reliability metrics, while investing in targeted local capacity building—training municipal engineers, digitising municipal services, and co-financing microgrid or fibre projects in underserved areas.

Sectors such as telecommunications and logistics can be particularly catalytic. More competitive telco markets lower costs for SMEs to adopt cloud services, enabling remote work, digital payments, and e-commerce participation—pathways to higher productivity. Better port modernisation and customs digitalisation reduce lead times for exporters and importers, making Philippine firms more attractive partners in ASEAN supply chains.

Practical measures to improve reliability—lessons for the region
Policymakers across Southeast Asia can draw practical lessons from Philippine and regional experiences:

• Track operational reliability metrics, not just installed capacity. Publish uptime statistics for utilities, average repair times, and frequency of interruptions. Transparent metrics create accountability and help target investments.
• Combine central grid upgrades with localised solutions. In the Philippines, microgrids, solar-plus-storage installations, and targeted distribution upgrades for island economies mitigate the higher costs and logistical challenges of archipelagic connectivity.
• Encourage competition and regulatory clarity in utilities. Relaxed foreign ownership rules in telecoms and investment-friendly frameworks for independent power producers (IPPs) tend to improve service quality and reduce prices over time.
• Digitise processes end-to-end. From online business registration to e-customs and digital permitting, procedural digitisation reduces the time firms spend navigating bureaucracy and complements improvements in physical infrastructure.
• Target SME support. Offer co-financing or subsidy schemes for SMEs to adopt reliable backup solutions in transition periods, paired with incentives to reinvest savings into productivity-enhancing technologies.
• Build resilience into logistics. Port congestion in Metro Manila, for instance, has pushed shippers to use alternative ports like Batangas, Cebu or Subic. Investing in multimodal connections and optimising hinterland links reduces the vulnerability of supply chains to local disruptions.

Broader implications across Southeast Asia

As ASEAN economies industrialise and expand services, the policy lens must shift from merely increasing infrastructure stock to ensuring its day-to-day stability. The Philippines’s experience underscores that predictability—measured in minutes of uptime, not gigawatts of capacity—drives firms’ decisions to invest and integrate into regional value chains.

Countries such as Vietnam, Indonesia, Thailand, and Malaysia face similar trade-offs: where headline projects boost national capacity, it is operational reliability that determines whether firms actually upgrade technology, hire more staff, or enter export markets.

Also Read: Are startups neglecting the future middle-class population in Philippines?

Investors, too, are sensitive to these dynamics. Multinationals assessing factory locations weigh not just cost and labour but the probability of meeting contractual uptime obligations. As Southeast Asia competes to move up the value chain into semiconductors, electric vehicle components and higher-end electronics, infrastructure reliability will increasingly be a differentiator.

Conclusion

The Philippines offers a practical blueprint for unlocking hidden productivity: elevate the operational reliability of infrastructure, pair physical upgrades with digital procedural reforms, and foster competitive utility markets. For Southeast Asia, the message is clear—investing in reliability is not a second-order concern; it is a central, silent engine that will determine whether the region can translate infrastructure projects into sustained firm-level productivity, broader regional integration and long-run economic growth.

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Walk into any co-working space, accelerator office, or industry event, and you will hear the incessant, almost hypnotic whisper: “Your network is your net worth.”

This axiom has morphed from a helpful reminder into the most sophisticated form of collective time theft in modern entrepreneurship. It pressures founders into a mandatory, ritualistic consumption of generalised networking with the endless coffee meetings, the sprawling conference cocktail hours, and the mastermind group sessions.

The reality is brutal: for the early-stage founder, the cost of maintaining a vast, shallow network far outweighs its actual commercial value. The majority of time spent networking is not productive; it is where founders go to feel validated instead of feeling the painful pressure of real customer acquisition.

This is the Networking-to-Death Trap, and it is quietly killing the focus, the runway, and the structural integrity of otherwise promising companies.

The opportunity overload

The core problem with generalised networking is that it generates opportunity overload.

Every handshake, every business card exchanged, every LinkedIn acceptance carries a micro-cost of processing. A founder who attends four events a week is bombarded with low-signal, high-volume inputs: invitations to irrelevant podcasts, pitches from overpriced service providers, and advice from mentors who solved a problem five years ago in a different market.

This noise does not lead to strategic insight; it leads to strategic paralysis.

Also Read: Networking is expanding, but execution still lags

The founder becomes addicted to the possibility of a major connection, chasing the mythical US$100 million deal that will come from a cold introduction, while neglecting the grinding, essential work that generates reliable, compounding revenue:

• Deep customer conversations: The only person who truly matters to your survival is the customer willing to pay a high price to solve their acute pain. These customers are not found at generalised networking events; they are found through focused, vertical outreach in their specific industry trenches.
• Core product fortification: Every hour spent debating the merits of a Decentralised Autonomous Organisation at a founder retreat is an hour not spent improving your product’s stability, security, or core feature set.

The generalised network promises immediate comfort and psychological support, but it delivers crippling distractions. It replaces the necessary, painful isolation of deep work with the soft, seductive illusion of being busy.

The power of vertical isolation

The most successful disruptive companies are built through selective, vertical isolation. This strategy focuses on generating leverage through depth, not breadth.

• Rule 1: Isolate the customer: Your network should not be comprised of other founders; it should be comprised of your ideal, paying customers and the two or three most influential experts who speak their language. Do not attend a “Tech Startup Mixer”; attend a conference for Heads of Supply Chain in the Cold Storage Industry, even if you are only selling software.
• Rule 2: The two-hour rule: The founder’s calendar should treat external meetings as a limited, precious resource. If the meeting does not directly result in a signed contract, a critical hire, or the resolution of a technical dependency within the next 90 days, it must be ruthlessly relegated to a low-priority slot or rejected entirely. The opportunity cost of a two-hour networking lunch is easily $500 worth of focused engineering time.
• Rule 3: Optimise for the asynchronous win: Leverage your community for knowledge exchange only when it is asynchronous and non-disruptive. Use highly specialised, focused online forums or private communication channels where the input is highly targeted and searchable. This allows the founder to extract the high-value insight without suffering the time commitment of a live conversation.

Also Read: What are some networking benefits that are essential for startups?

The general community operates on the principle of serendipity, where the hope is that a random encounter will solve your biggest problem. The serious founder operates on the principle of design, meticulously engineering the exact inputs needed to reach a specific, immediate business outcome.

Founders need to be ruthlessly honest about their calendar. Most networking is a sophisticated form of peer review, where founders seek validation from each other to avoid the terrifying, objective judgment of the market. The time spent collecting hundreds of shallow contacts is time stolen from building the product so unique that those contacts would eventually chase you.

Look at your calendar for the last month. How many hours were spent in meetings or events that generated revenue, versus hours spent in meetings that generated only optimism? If your company’s survival depends on the random chance of a handshake, is your business model strong enough, or are you mistaking socialising for strategy?

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After facilitating leadership workshops across different organisations, I began noticing something interesting. Some groups needed constant encouragement just to participate, while others required only a small prompt before the room filled with energy, debate, and shared thinking.

The difference was rarely about intelligence, seniority, or company size. It was about maturity – not the age of the team, but how they think and learn together.

Over time, three patterns consistently emerged whenever I worked with mature teams.

Mature teams are noisy, in the right way

The first sign is noise. Productive noise.

When working with mature teams, a simple question from the facilitator often sparks layered conversations almost immediately. Participants challenge assumptions, build on each other’s ideas, and connect discussions to real operational experiences. The session stops feeling like a workshop and starts resembling an executive conversation already in motion.

This happens because experienced teams carry accumulated reflections into the room. They have navigated crises, misaligned strategies, and difficult decisions before. Training becomes a space to recalibrate rather than to discover leadership for the first time.

More importantly, people speak freely. They are less concerned about appearing wrong or disagreeing publicly because psychological safety already exists within the group. Members trust that expressing a view will not damage relationships or reputations.

For facilitators, this is often the moment when leadership development becomes meaningful. Engagement is no longer driven by exercises; it is driven by ownership. The team treats learning as part of work itself, not an interruption from it.

Also Read: Why Southeast Asia’s edutech must go beyond chatbots to truly transform learning

Mature teams disagree without breaking trust

Another reliable signal of maturity is visible disagreement.

In younger teams, silence is often mistaken for alignment. Participants defer to hierarchy or wait for consensus before contributing. Mature teams behave differently. Diverse opinions surface quickly, and opposing viewpoints are voiced openly.

I teach improv-inspired communication principles, particularly the idea of “yes, and.” Interestingly, mature teams rarely respond with immediate agreement. Instead, they demonstrate a more nuanced version of it. Someone may acknowledge a colleague’s reasoning, introduce an alternative perspective, and then carefully connect both ideas before advancing their own position.

The conversation becomes additive rather than adversarial. Participants feel heard even when opinions diverge, and debate turns into collective sense-making instead of personal validation.

In many leadership trainings, organisations aim to build psychological safety. Mature teams reveal what it actually looks like in practice: trust strong enough to allow challenge without fragmentation.

Mature teams expand the learning beyond the curriculum

My favourite indicator appears when the workshop stops belonging solely to the facilitator.

Mature teams rarely stay confined within the training framework. Participants bring their own vocabulary, experiences, and intellectual references into the discussion, effectively co-creating the learning environment.

In one session on DISC profiling, a participant introduced the concept of analysis paralysis to explain behavioural patterns we were observing in decision-making styles. In another workshop on growth mindset, someone connected the discussion to the psychology of the Dark Triad, reframing leadership behaviour through a completely different lens.

These moments signal something important. Participants are not absorbing content passively; they are integrating it into their own mental models. Learning becomes translation, not repetition.

Also Read: I came back to coding after 20 years, and the fault line on my team was nothing like I expected

Early in my facilitation journey, such moments made me slightly uneasy. Over time, I learned that effective facilitation sometimes means becoming a student in real time, asking participants to elaborate, and letting expertise move in multiple directions rather than one.

When teams feel safe enough to extend the curriculum, leadership development shifts from instruction to collective intelligence.

Maturity is not about time

Team maturity is often mistaken for tenure or organisational age. Yet I have seen long-standing leadership teams remain cautious, while young startup teams demonstrate remarkable openness and ownership.

Maturity is visible in how teams operate daily: how they challenge one another, how they learn together, and whether individuals feel responsible for outcomes beyond their own roles.

The most effective leadership training does not create maturity overnight. Instead, it surfaces conversations teams have avoided, accelerates alignment that is still forming, and gives structure to trust that is already emerging.

And perhaps this is where the real question sits for leaders and organisations: what kind of leadership development do you actually bring into your team? Not just to transfer knowledge, but to shape how your people think together, disagree safely, and build on each other’s ideas in real time.

Because mature teams are not an accident. They are intentionally cultivated through the right conversations, the right reflections, and often, the right facilitation that helps those moments emerge and stick.

And perhaps that is the real goal of any leadership development effort: not merely to teach new skills, but to help teams grow into environments where learning, disagreement, and contribution happen naturally – with the right support to make those moments visible, and meaningful, when they matter most.

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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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100×100 (previously Wavemaker Impact), the Singapore-based climate company builder behind a clutch of Asia-focused startups, has launched a US$100 million second fund to assemble and scale 50 new firms across Southeast Asia and India.

The move cements a growing trend: investors and builders are shifting from pure software bets to hands-on venture creation to turn climate technologies into commercially viable businesses in the region.

A builder, not just a backer

100×100’s model is deliberate and prescriptive. Rather than seeding existing early-stage startups, the firm partners with seasoned entrepreneurs to co-found companies from scratch. Each venture is designed with an explicit commercial target, US$100 million in revenue, and an emissions abatement ambition of 100 million metric tons of carbon dioxide equivalent over its lifetime. The firm says it will focus on energy, food, materials, and supply-chain sectors where demand, geopolitical shifts, and resource constraints are reshaping markets.

Also Read: Climate tech’s shift from doing good to doing well

Founding partner Marie Cheong framed the thesis succinctly: “Our name reflects our conviction that profit and carbon reduction are not a trade-off, but a multiplier. With Fund II, we are doubling down on a demonstrated strategy with a platform that is ready-to-go.” The quote underlines 100×100’s pitch: build ventures that exploit a ‘green discount’, delivering cost or economic advantages while cutting emissions.

Why Southeast Asia and India matter

The Southeast Asian and South Asian markets are central to 100×100’s strategy for several reasons. The region accounts for a considerable share of global emissions from agriculture, industry, and power generation, while simultaneously undergoing rapid industrialisation, manufacturing reshoring, and infrastructure expansion. These dynamics create fertile ground for technologies that lower costs and emissions, especially those that can be embedded into supply chains or scaled across large, fragmented markets.

Quentin Vaquette, another founding partner, bluntly argued the regional case: “Southeast and South Asia sit at the intersection of the world’s most urgent challenges, holding a disproportionate share of global emissions while increasingly becoming a key region for manufacturing reshoring, AI infrastructure buildout, and food system redesign.”

100×100’s stated target is ambitious: collectively, the ventures it builds should reduce 10 per cent of global emissions, a claim that signals the scale it intends to pursue.

Track record and traction

Fund II follows Fund I, which closed at a US$60 million hard cap in 2023. The firm says its initial cohort has produced fast-moving results: 27 co-founded companies across eight countries, a portfolio survival rate nearly double the median venture capital average, and portfolio companies operating at roughly 1.5 times greater capital efficiency than typical VC-backed startups. External validation includes demand from institutional and strategic backers such as the US Development Finance Corporation, Singapore’s Economic Development Board, and British International Investment, among others.

Also Read: Funded: SEA climate tech has US$1.1B and a problem no one wants to name

The portfolio has produced concrete commercial cases. Rize, a company that reduces methane emissions in rice cultivation and serves smallholder farmers, reportedly delivered US$11 million in revenue in 2025 with 550 per cent year-on-year growth, while improving livelihoods for over 40,000 farmers. Helios, a residential solar provider in the Philippines, is cited as growing at more than 40 per cent month-on-month over the past year. Such metrics are central to 100×100’s argument that its venture-building model accelerates commercial product-market fit faster than traditional funding routes.

How the venture builder works

100×100 employs a structured process to locate white spaces in high-emissions sectors. The firm says it speaks to over 1,000 experienced founders each year to identify operators with domain expertise who can lead scalable companies from day one. It takes larger equity stakes than traditional VCs and embeds operational support and playbooks into the early stages, effectively trading a higher level of involvement for quicker, more efficient scaling.

The ‘green discount’ concept is worth unpacking: rather than positioning climate products as premium add-ons, 100×100 seeks ideas that reduce unit costs or improve productivity while simultaneously lowering emissions, making them more likely to be adopted at scale in price-sensitive markets across Southeast Asia and India.

Regional implications and challenges

100×100’s expansion is timely, but not without obstacles. Commercialising hardware-intensive or industrial-process innovations in Southeast Asia involves high capital intensity, complex supply chains, and regulatory fragmentation. Local partners, distribution networks and long sales cycles, especially for industrial and agricultural customers, remain hurdles. The firm’s playbook of co-founding companies with experienced founders and taking larger equity stakes is explicitly designed to mitigate some of these operational risks.

Also Read: ‘The next generation of unicorns will be from greentech’: Wavemaker Impact’s Steve Melhuish

For Southeast Asia, the model offers potential benefits beyond emissions reductions. If the builder can scale firms that reduce costs for manufacturers or farmers, the region could capture more value in emerging supply chains, particularly as multinational companies reorient production away from concentrated centres. That could translate into job creation, technology transfer, and greater regional resilience.

What investors are buying

Institutional backers of Fund II are effectively betting on a repeatable engine for climate company creation in markets that are often seen as difficult for pure-play climate tech investors. The claim of near-top-quartile performance, higher capital efficiency, and rapid revenue growth will be closely watched by LPs seeking both impact and returns. Whether the model can consistently produce US$100-million revenue companies and deliver massive abatement across 50 new ventures remains to be proven at scale.

Closing thoughts

100×100’s US$100 million Fund II is a bold bet that the most impactful climate solutions in Southeast Asia and India will emerge from active venture building rather than passive capital allocation. Its early results offer reasons for cautious optimism: fast revenue growth, strong founder engagement, and a focused sector strategy. The coming years will test whether the studio’s playbook can convert ambitious climate and commercial targets into a pipeline of durable, regionally rooted companies that reshape how emissions-intensive industries operate across Asia.

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In my previous article, I traced how the companies that built the internet lost the distinct cultures and identities that made them formidable, optimised into homogeneity by the same growth logic that made them successful. Now I will move to examine how AI has impacted this process. 

The first thing to understand about Big Tech’s AI pivot is that it was not a strategic decision.

Across all AI-centric announcements in Big Tech, the language is identical: Transformative. At scale. Across our entire product surface. It’s not just the language; the investor calls, consumer messaging, acquisitions, and internal decks all follow the same patterns and ideas. 

Every major technology company is now, officially, an AI company, having arrived at this conclusion within roughly the same window, having made roughly the same infrastructure bets, and having restructured roughly the same proportion of their workforces to fund them. The probability that this represents independent strategic convergence of all these organisations is frankly ridiculous. They are making strategic calls simply based on what their competitors are doing to maintain an illusion of “competitiveness”. 

René Girard, the French anthropologist and literary theorist, called it mimetic desire: the observation that humans do not independently decide what to want, but want what they see others wanting. Applied to organisations, it produces what he called a mimetic crisis, which refers a state in which rivals, having imitated each other into near-identical positions, stop competing over what is actually valuable and start competing over the appearance of competition.

This is supported by DiMaggio and Powell, who observed that organisations under pressure from external stakeholders (like investors) don’t innovate, but imitate the competition, converging on whatever behaviour their peers have already legitimised, purely because it’s seen as safe and deals with the anxiety of innovation uncertainty. 

The AI pivot is, therefore, a mimetic crisis at an industrial scale. The fear of missing a rival’s gain has become more powerful than any assessment of whether the gain is real. 

The AI pivot is, by any honest reading, a mimetic crisis. Every major technology company is now, officially, an AI company. The announcements are indistinguishable, and all contain the same words in the same order: transformative, at scale, across our entire product surface. What is harder to find, in most cases, is evidence that this is producing value for the people who use the products.

Studies from Goldman Sachs and MIT have questioned whether enterprise AI spending generates productivity gains proportionate to the investment. The technology is being deployed ahead of demonstrated value, which is not a description of a confident strategy. It is a description of mimetic panic, as nobody wants to be the company that missed the wave.

Meanwhile, the jobs supposedly being augmented by AI are being eliminated by the same companies making the loudest noise about AI’s transformative potential. The mimetic crisis requires a resolution, and Girard argued that resolution historically comes through the scapegoat mechanism wherein the community unifies around the sacrifice of an arbitrary victim, temporarily relieving the tension of rivalry.

Also Read: A 65% probability explains the next likely move for Bitcoin as leverage clears

In corporate terms, it refers to the moment when the AI bubble deflates, and a single company is singled out as the cautionary tale while the larger players quietly reassess this investment.

What the bubble produces

To understand where mimetic capital flows when it chases a narrative ahead of validated products, consider Cluely.

Cluely was founded in 2025 by two Columbia University students, one of whom had been expelled for using an earlier version of the tool to cheat through an Amazon technical interview. The product was honest about this: an AI overlay that sits invisibly on your screen during video calls and feeds you answers in real time.

Within months, it had raised US$5.3 million in seed funding, then a US$15 million Series A led by Andreessen Horowitz, valuing the company at approximately US$120 million. All of this, for a company with an unclear business model, a viral marketing strategy, and revenue numbers that turned out, in March 2026, to be fabricated. The CEO admitted on X that the US$7 million ARR figure he had given to TechCrunch the previous summer was a lie.

The company has since rebranded as an AI meeting note-taker.

Cluely is not an anomaly, but is a case study in what DiMaggio and Powell’s mimetic isomorphism looks like at the level of venture capital: under conditions of uncertainty, investors copy other investors. As the field signals that this is the kind of company worth investing in, the logic of missing out becomes more powerful than the logic of performing due diligence. By 2025, AI captured close to 50 per cent of all global venture funding: over US$200 billion, up from 34 per cent the year before.

The terror of being the one who missed the next OpenAI has become more powerful than any assessment of individual companies. The object of desire ( AI value) has receded and has been replaced by an obsession to win the investment war.

The most significant threats to the monoculture are being built by companies quietly constructing alternatives to the platforms the monoculture depends on.

Linux desktop usage has historically been a running joke: it’s always the year of Linux on the desktop, but never quite. Framework, a new age PC and laptop manufacturer with a distinct cultural identity around repairability, now ships laptops with Linux as a first-class option, which sold out prior to its Windows counterpart.

Lenovo ThinkPads come with Linux pre-installed. The developer community has shifted significantly toward Linux-based workflows. This is not yet a mass-market story, but it represents, in DiMaggio and Powell’s terms, coercive isomorphic pressure running in reverse: an external force that makes the dominant platform’s continued stagnation costly, rather than safe.

The gaming front is more significant. Windows has historically been the stickiest consumer use case: the reason people tolerated everything else about it. Valve’s Steam Deck changed that. A Linux-based device that runs an enormous proportion of the Steam library with fewer power requirements.

SteamOS is now expanding beyond the Deck due to its contributions to the WINE platform and the Proton compatibility layer (both of which are free). Valve quietly built a gaming ecosystem that no longer requires Windows to function. That is a disruption in the structural sense, which has led to Windows and Xbox’s newfound focus on improving their services as of late.

Also Read: Emotional intelligence makes AI training stick

The reckoning

The current market is an example of DiMaggio and Powell’s iron cage: organisations that have become so optimised for legitimacy to appear correct to investors, regulators, and each other, that they have lost the ability to actually be anything in particular.

The companies that built the internet had something the current iteration has optimised away: cultural density, the very thing that makes a company relatable and marketable to consumers in choice-based markets like consumer technology.

Girard was clear about how mimetic crises resolve: scapegoats. The dot-com correction produced its scapegoats in companies like Pets.com and Webvan. The scapegoat is never the system itself, but an organisation within the system, selected to make the system appear self-correcting. Cluely may serve this function, or it could be a tech giant, or another one of the thousands of new-age tech companies. 

What breaks the cycle is the alternative. Valve didn’t announce a strategy to challenge Windows; it simply built a device, then a platform, then an ecosystem, until the alternative structurally competes with the market leader. Framework didn’t publish a manifesto; it simply made excellent products in line with its simple mission of repairability and customisability.

The irony is that isomorphism has made differentiation easier, not harder. When every large tech company looks, sounds, and feels the same, being genuinely excellent at something specific and being honest about what you believe in is a competitive advantage of unusual power. The only problem is that the conditions that produce that kind of strangeness are precisely what institutional success destroys. 

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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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Singapore venture firm Tin Men Capital has invested in enterprise AI startup Pints AI as part of a US$5.6 million funding round, underscoring growing investor appetite for practical, business-facing artificial intelligence across Southeast Asia.

The capital will be deployed to beef up Pints AI’s engineering bench, tighten governance and audit frameworks, and improve internal systems. These priorities reflect a broader shift among corporate buyers away from experimental AI pilots toward production-grade solutions that can be audited, scaled, and trusted.

A test of enterprise readiness

Pints AI positions itself in a crowded but increasingly differentiated market: vendors that pair machine learning capabilities with tools and processes designed for enterprise deployment. Unlike consumer-facing generative AI products that attract headlines, enterprise AI’s value is measured in reliability, ease of integration, and regulatory compliance — attributes that command premium attention from both customers and investors.

Also Read: Enterprise AI hits barriers as privacy, sovereignty demands grow

The fresh funding arrives at a juncture when businesses across Southeast Asia are accelerating digital transformation efforts to control costs and streamline operations. Companies, from logistics providers in Indonesia to banks in the Philippines, are seeking automation that can reduce manual workflows, improve decision speed, and surface insights from fragmented data. That demand has pushed investors towards startups that solve concrete operational problems rather than consumer novelty.

Tin Men Capital’s participation signals confidence in Pints AI’s ability to bridge that gap. The Singapore-based firm has been active across the region, focusing on technology companies that can scale within Southeast Asia’s diverse market landscape. The deal aligns with a wider trend among regional VCs to back B2B software and infrastructure companies that promise recurring revenue and enterprise-grade robustness.

Engineering and governance: where the money is going

According to Pints AI’s announcement, most of the capital will be allocated to hiring engineering talent and strengthening governance and audit systems. Those priorities reflect hard lessons from companies attempting to deploy AI at scale: models and pipelines need constant upkeep; data lineage, explainability, and compliance provisions must be baked into product design; and incident response and monitoring must be operationalised.

For Southeast Asian customers, those capabilities matter more than ever. Many markets in the region are still developing regulatory guardrails for AI-driven decision-making, while firms juggle cross-border data flows, varying security standards and tight budgets. Startups that can demonstrate strong governance frameworks and the ability to show auditors and regulators what happens inside their systems will have an advantage in winning larger contracts from enterprises wary of reputational and compliance risk.

This emphasis on “trustworthy AI” is reshaping product roadmaps across the sector. Investors are increasingly willing to finance not just model development but the scaffolding required to deliver AI reliably into complex enterprise environments: observability tooling, automated testing, role-based access, and audit trails.

Southeast Asia: fertile ground for practical AI

Southeast Asia’s startup ecosystem has matured considerably in the past decade, moving from consumer-focused plays to more diversified portfolios that include fintech, enterprise software, cybersecurity, and healthtech. That maturation is accompanied by a more selective investor base: with public markets and macro uncertainty tempering valuations, VCs are concentrating capital on companies with clear unit economics, defensible distribution channels and demonstrated product-market fit.

Also Read: The big flip: Why being “smart” isn’t enough for enterprise AI in 2026

Enterprise AI fits that profile for a number of reasons.

• First, the addressable market is large: SMEs and corporates across ASEAN carry vast amounts of unstructured data and manual processes ripe for automation.
• Second, enterprise contracts often translate into higher and more predictable annual recurring revenue compared with consumer apps.
• Third, Asian firms are under pressure to improve margins and efficiency, a secular driver for automation investments.

Yet the path to scale is not straightforward. Startups must navigate diverse languages, regulatory regimes, and legacy IT stacks. Winning in Southeast Asia frequently requires localised approaches and partnerships, something that investors like Tin Men with deep regional networks can help facilitate.

What the deal means for the sector

The Pints AI round is indicative of several converging trends. It reflects continued interest in AI beyond flashy, consumer-oriented use cases; it highlights the premium on engineering headcount and compliance tooling; and it underscores the growing role of Singapore-based investors in shaping the regional enterprise software landscape.

For founders in the region, the signal is clear: investors are ready to fund the work required to move AI from lab experiments into enterprise standard operating procedures. That work is expensive and often less glamorous than training large models, but it may deliver more durable returns as customers prioritise reliability and auditability.

What remains to be seen is how quickly the demand translates into large, repeatable contracts. Southeast Asian enterprises are still at various stages of digital maturity. Some will adopt packaged automation solutions readily; others will require bespoke integration and longer sales cycles. The companies that succeed will likely be those that combine strong engineering teams with pragmatic go-to-market strategies and an ability to prove outcomes.

Implications for founders and investors

For founders building enterprise AI, the takeaway is to prioritise the plumbing: observability, testing, data governance and customer success functions that can demonstrate ROI. For investors, Pints AI’s funding round is another data point suggesting that capital will follow companies that can reduce operational friction for customers and withstand the scrutiny of auditors and regulators.

Also Read: The psychology of AI adoption: How familiarity bias is quietly slowing finance down

As Southeast Asia’s economies digitise and the regional adoption of AI grows, there will be more room for companies that deliver measurable efficiency gains. The Tin Men-Pints AI deal shows that investors are willing to back the less glamorous but essential work of industrialising AI for business, a necessary step if the region’s AI ambitions are to translate into sustained commercial impact.

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Most Indonesian millennials who grew up in the 1990s remember the sentence from their elementary Bahasa Indonesia textbook: “bapak pergi ke sawah, ibu masak di dapur.” In English, it is translated as “father goes to the field, mother stays home to cook.” Decades later, that sentence has quietly become a cultural script. The term bapak tani, the male farmer, remains the dominant image attached to Indonesian agriculture, celebrated in policy, portrayed in media, and assumed in market design. Ibu tani, the woman farmer, exists in the field but rarely in the narrative.

Across Indonesia’s agricultural landscape, women make up a significant share of the farming workforce, planting, harvesting, and processing, yet remain systematically excluded from the decisions, markets, and profits. As Prof. Anna Fatchiya of IPB University’s Faculty of Human Ecology has noted, women farmers carry significant roles in the agrifood system, yet receive disproportionately little recognition for it. They are present in the field, but absent from the value chain. This is not a cultural accident. It is a structural one, and no agritech platform is designed to fix the root cause of this gender problem.

The numbers make this concrete. Data from Syngenta Indonesia reveals that 37.8 per cent of Indonesian farmers are women, yet only 13.61 per cent hold formal rights over the land they work. In Jambi province, Sumatra, women farmers earn approximately US$3.53 per day compared to US$6.48 for their male counterparts, for the same work and on the same schedule. According to Widyani (2023) of Universitas Negeri Makassar, these disparities are sustained by deep-rooted patriarchal norms, belief systems inherited from the past that continue to structure present agricultural relations.

The problem is not the app — it is the system behind it

In rural Indonesia, patriarchal structures are active and embedded in how agricultural markets function. Male farmers are treated as the default market actors; mostly, they negotiate with middlemen, secure supplier relationships, and are recognised as the primary decision makers. Female farmers, meanwhile, are limited to planting and harvesting, while domestic responsibilities such as cooking and taking care of the family fill the rest of their hours.

This is not a matter of attitude or culture. It is how informal market rules have been built. Middlemen seek male farmers because male farmers are considered the leaders. Price information circulates through networks that women are excluded from. Nobody wrote these rules down. They did not need to. The norms came first, and the market followed.

Also Read: Women in tech: It’s time to reframe the conversation

Land ownership in rural Indonesia is dominated by men, aligned with data from FAO (2018) that globally, less than 15 per cent of women own agricultural land. Further, access to agricultural credit typically requires proof of land rights. Women farmers without formal legal assets cannot participate in programs such as Kredit Usaha Rakyat (KUR) arranged by national banks and the government in Indonesia. 

This is where the gaps in agritech become visible. Agritech ventures enter the market with two goals:

• To grow, through user acquisition, partnerships, and revenue, and 
• To solve problems in the agricultural supply chain. 

Both goals are reasonable. But both are designed to operate within the current system, not to challenge it. When the existing system already excludes women farmers at the level of norms, informal rules, and formal structures, a platform that adapts to that system will digitise the exclusion that was already there.

Technology is not the problem. The foundation it is built on is.

What fixing the system actually looks like 

Fixing the market system does not start with building a better app or AI tech. It starts with changing who gets to participate in the market. By design, this is where civil society organisations (CSOs) play a role. 

CSOs are often treated as service delivery channels, organisations that distribute aid, run training programs, and report impact numbers. But in agricultural market systems, they are more important than that. They operate inside communities, build trust over years, and are positioned to shift the informal rules that formal institutions and tech platforms cannot do.

Also Read: AI could redefine women in the workplace—and companies must act now

Perempuan Sumatera Mampu (PERMAMPU) and Pemberdayaan Perempuan Kepala Keluarga (PEKKA) are two examples of CSOs that aim to enhance women’s economic independence and leadership, aligned with the needs of women farmers in Indonesia to have access to a business and market environment. 

However, to strengthen and widen the impact of CSOs, there must be clear collaboration between stakeholders, such as:

• Collaboration with agritech startups to make women farmers become reliable producers. 
• Collaboration with local authorities to enable them to connect with national banks in order to access credit for enterprises such as KUR. 
• Collaboration with universities to access knowledge transfer from academia and researchers. 

These efforts will not only produce results on paper alone, but also revolutionise the system to put women farmers as reliable producers and partners. 

For founders and investors in Southeast Asian agriculture, the question is no longer whether women farmers are underserved. The real question is whether the ecosystem is willing to measure success by equal opportunities, not just equal access. 

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

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We are living through a grand paradox. On one hand, global summits like COP and the G20 scream that trillions of dollars are needed to hit the UN Sustainable Development Goals (SDGs) by 2030. On the other hand, I sit in meetings with brilliant founders in Jakarta, Ho Chi Minh City, and Colombo who are building physical, atoms-not-bits solutions, and they are starving for capital.

Why? Because the funding gap isn’t just about a lack of money. It’s a fragmentation of intelligence. After reviewing 10,000 pitch decks and holding 5,000 founder meetings, I’ve realised that we’ve built a system so bureaucratic and dysfunctional that the very people we need to save the planet are spending 40 per cent of their time navigating a capital labyrinth instead of engineering solutions for goal 7 (clean energy) or goal 13 (climate action).

The three invisible barriers killing hardtech impact

When you build a SaaS platform, the path is linear: Seed, Series A, Series B. But when you build a physical solution—like a botanical biorefinery in Bali or a blue carbon platform in Vietnam—the software playbook fails. Here is why:

• The compliance tax and the reporting trap

Many impact funds, especially those tied to Multilateral Development Banks (MDBs) or large NGOs, come with strings that would choke a late-stage corporate, let alone a five-person startup.

I’ve seen founders win a US$150k grant only to realise they need to hire a full-time compliance officer just to manage the quarterly reporting metrics required by the donor. This is a resource drain that favours consultancy-style startups over engineering-style startups. We are inadvertently funding people who are good at writing reports, not people who are good at fixing the ocean.

• Geopolitical and sector-specific information deserts

The capital stack is not a ladder; it’s a spiderweb. To survive, a founder needs to weave together:

• Technical assistance (TA): Like the Blue Carbon Accelerator Fund (BCAF) for feasibility.
• Non-dilutive grants: Like the USAID-funded Climate Solutions or the IUCN’s biodiversity calls.
• Concessional debt: From foundations like Beneficial Returns or The Rockefeller Foundation.
• Equity: From VCs who actually understand hardtech lifecycles.

The dysfunction: Currently, there is no single source of truth. A founder in Vietnam building a regenerative aquaculture system has to search through 1,000 PDFs and closed-door networks to find these sources. If you don’t have a sherpa or an expensive consultant, you simply don’t find the money.

• The atoms vs bits valuation friction

Standard VCs want 10x returns in five to seven years. Physical science-led solutions (deeptech) often need 10 years just to reach commercial scale. Because founders don’t understand the capital stack, they often take VC money too early, get diluted into oblivion, and the company collapses under the weight of software-speed expectations.

Also Read: Why I’m trading bytes for atoms: The 65-year-old investor breaking the climate tech silos

The real-world friction: Two scenarios

To show how broken this is, let’s look at the theoretical paperwork mountain founders face today:

• The blue carbon play (Indonesia/Vietnam): A founder building an IoT-verified mangrove restoration needs US$2M. They find a potential grant from the Global Environment Facility (GEF). But the GEF requires a government endorsement letter. The founder spends six months in ministerial waiting rooms in Jakarta, only to find the grant window has closed. They then pivot to a CVC (Corporate Venture Capital) play, but the CVC won’t move until there is a first-loss guarantee from an NGO. The founder is now a full-time diplomat, not an entrepreneur.
• The agtech engineer (India/Sri Lanka): A founder has a low-cost, solar-powered biorefinery. They look at the Asian Development Bank (ADB) funds. The ADB is massive, but the entry point for a startup is invisible. They end up chasing impact-linked loans where the interest rate drops if they hit SDG targets. It sounds great, until they realise the verification audit costs more than the interest savings.

The solution: A call for information infrastructure

We are architecting capital, but we haven’t yet architected the information portal to deliver it.

I am calling on my fellow fundraising angels, investors, and the tech community: We must build a global impact portal. We need a searchable, AI-driven command centre where a founder can type: “I am a startup in Vietnam, building a seaweed-based plastic alternative. Show me every grant, technical assistance provider, NGO loan, and Hardtech VC active in my region right now.”

If we can build complex algorithms to predict what movie you want to watch on a Friday night, we can certainly build a directory that helps a climate-tech founder find a grant in Bali or a lab in Colombo.

Also Read: Why perfect carbon audits could cripple climate finance — and what to fix instead

The founder’s cheat sheet: Five questions to ask before taking your first dollar

Before you sign that term sheet or spend six months on a grant application, ask yourself these five questions to ensure you aren’t walking into a trap:

• Is this patient or pressured capital? Does the funder understand that hardware takes 3x longer than software? If they expect a pivot to SaaS in 18 months, run.
• What is the compliance-to-capital ratio? Will the reporting requirements for this US$50k grant cost you US$60k in engineering hours and administrative overhead?
• Does this money unlock the next level of the stack? Will this grant provide the Technical Assistance (TA) needed to make you bankable for a concessional loan later?
• Are you solving for the goal or the grant? Are you tweaking your technology just to fit a specific NGO’s mandate, or does the funding truly support your core engineering roadmap?
• Is there a first-loss guarantee? Can this foundation or NGO provide a guarantee that makes it safer for a commercial bank or VC to follow them?

The status quo is a tax on our future. To the governments and the NGOs: Simplify your entry points. To the founders: Stop being accidental fundseekers and start being architects of your own capital stack.

Let’s stop talking about the gap and start building the map.

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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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AI skills have shifted from specialist niches into mainstream hiring practices, with AI‑related job postings leaping to capture 5.3 per cent of all roles in 2025, up from 3.3 per cent the year before.

That rise represents roughly 30,000 additional listings in a labour market otherwise facing global headwinds, according to PwC’s analysis of job-posting data and government surveys.

The numbers underline a broader transformation: rather than phasing out roles, AI is reshaping them. Occupations that are more exposed to AI, where day‑to‑day tasks and core abilities overlap with AI capabilities, are seeing more job openings and a faster rate of skill turnover.

Also Read: AI will replace inertia before it replaces people

The trend has important implications for Singapore, which aims to remain Southeast Asia’s technology and finance hub, and for neighbouring markets that look to the city‑state as a bellwether.

AI exposure correlates with job growth and skill churn

PwC’s measure of AI exposure shows a clear pattern: the greater an occupation’s exposure, the larger the number of job postings. Between 2019 and 2025, there was a +0.30 correlation between AI exposure and net skills change, suggesting that occupations more entwined with AI are also evolving fastest in terms of required competencies.

The reshaping is visible in hiring data. AI‑related roles rose to about 84,000 in 2025, an increase from the previous year, and over half of all job postings now fall within occupations with higher AI exposure. This suggests that employers are not merely replacing human tasks with automation; they are redesigning job descriptions and adding new responsibilities that include working alongside AI tools.

Public sector, finance and tech lead hiring

Sectoral analysis shows that technology, media, and telecom (TMT), government and public sector, and financial services are leading AI hiring in Singapore. These sectors also report high rates of AI adoption in surveys by the Ministry of Manpower (MOM), where 18.9 per cent of firms said they were redesigning job functions and 13.9 per cent reported creating new AI roles in the first quarter of 2026.

The government and public sector, in particular, are offering large wage premiums for AI talent, with advertised wages approximately 107 per cent higher for AI‑related roles than for non‑AI roles in the same sector in 2025. Consumer Markets reported a 96 per cent premium. High premiums in lower‑volume sectors point to concentrated demand for specialised skills. In contrast, more broadly, AI‑enabled sectors show narrower pay gaps as AI becomes part of routine job requirements.

AI users, not just developers

A striking signal of mainstreaming is the concentration of demand. About 82 per cent of AI‑related job postings in Singapore are for AI user roles — non‑specialist or hybrid positions that require working fluency with AI tools — rather than for developers. AI user roles accounted for approximately +26,000 of the increase in postings, while developer roles added around +4,200 in 2025 versus the prior year.

This split shows employers favouring a model where AI augments existing workforces rather than remaining the preserve of elite engineering teams. For Southeast Asia’s startups and fast‑scaling firms, that means hiring managers will increasingly prioritise candidates who can blend domain expertise with practical proficiency in AI tools, rather than recruiting only core machine‑learning engineers.

Policy and upskilling: Singapore’s push and regional spillovers

Singapore’s policy moves in 2026, from a National AI Council to dedicated AI missions and an AI Impact Programme, underpin this labour market shift. Those initiatives aim to boost adoption across sectors and encourage workforce upskilling. As organisations transition from pilots to scaled deployments, the demand for job redesign and structured reskilling will only ratchet up.

Also Read: AI’s first real casualties: The tech jobs that vanished in 2025

For the region, Singapore’s policy and market signals matter. Regional governments and corporations often benchmark against Singapore, and multinational firms based in the city serve as hubs for talent and investment that spill over into Indonesia, Vietnam, the Philippines and Malaysia. Startups in those markets could both benefit and face talent competition as Singapore firms soak up AI‑literate candidates and pay premiums for specialised roles.

What this means for startups and talent markets

For startups across the region, several practical consequences follow:

• Hiring strategy: Expect competition for AI‑literate generalists. Startups will need clearer role definitions that combine domain knowledge with AI fluency and may have to offer training pathways rather than expecting fully formed skills.
• Costs and pricing: As wage premiums persist for specialised AI roles, early‑stage firms may face higher personnel costs or choose to outsource AI development to contractors and partner firms in lower‑cost markets.
• Upskilling and retention: Investing in internal reskilling programmes could become a cost‑effective alternative to poaching senior AI talent, especially where long‑term cultural fit and domain expertise are critical.
• Product roadmaps: Startups that embed AI into their core propositions, not merely as an add‑on feature, will be better positioned to attract customers and talent in an ecosystem where AI capability signals competitive parity.

Risk and governance

As roles proliferate, governance becomes central. PwC highlights AI governance frameworks as one way to manage risk and foster trusted deployments. For Southeast Asian firms, adopting governance standards early could reduce regulatory friction and build user trust across markets where consumer privacy and algorithmic fairness are growing concerns.

The regional picture

Singapore’s labour market is the most visible example in the study, but the underlying dynamics are relevant across Southeast Asia. Countries with maturing digital economies will see similar shifts, albeit tempered by local talent supply, wage structures and policy timelines. For regional policymakers and startup founders, the imperative is clear: investing in reskilling and responsible AI practices now will determine who captures the productivity gains of the next wave.

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For most of my adult life, I have been paid to write. That sentence used to carry a certain clarity. It meant you could report, interview, structure arguments, meet deadlines, understand audiences and turn half-formed ideas into something people wanted to read. Over the years, I have written for media platforms across Asia and globally, worked with founders, PR teams, editors, startups and business leaders, and seen how the definition of a “good writer” changes depending on the publication, market and moment.

Then generative AI arrived in the mainstream. This is no longer a niche productivity shift. McKinsey’s 2025 global AI survey found that 88 per cent of organisations now report regular AI use in at least one business function, up from 78 per cent a year earlier. For content and communications teams, that means AI-assisted writing is quickly becoming part of the operating environment, not a novelty

Today, ChatGPT, Gemini, Perplexity and a long list of other tools can produce a clean first draft in seconds. They can summarise research, suggest headlines, rewrite copy, generate social captions and mimic the structure of a thought leadership article. For hiring managers in media, PR, marketing and content, this raises an uncomfortable but necessary question: if everyone can now “write”, who is actually a good writer anymore?

More importantly, who is still worth paying to write? This is not a question only for editors or agency leads. It is increasingly an HR question. Across APAC, where companies operate across multiple languages, cultures, regulatory environments and media markets, the ability to communicate clearly is becoming more important, not less. But the signals we use to assess communication talent need to change.

A polished writing test is no longer enough. A portfolio full of neat articles is no longer enough. Even years of experience may not mean what it used to. The real differentiator now is not whether someone can produce words. It is whether they can think, judge, question, adapt and take responsibility for what those words do.

The old markers of writing talent are becoming weaker signals

Three years ago, if I were hiring a writer or content person, I would have paid close attention to bylines, writing samples, industry exposure, speed and grammar. These things still matter, but they are no longer sufficient. A candidate can now submit a clean sample with very little original thinking behind it. They can use AI to improve sentence flow, generate article structures or create a competent-looking draft on a topic they barely understand. This does not make them dishonest. In many cases, it simply reflects the new reality of work. Most content teams are already using AI in some form, formally or informally.

The problem is that hiring processes have not caught up. Many companies still assess writers as though the main scarcity is sentence construction. But in 2026, sentence construction is becoming cheaper. What remains scarce is judgment.

Can this person tell when a claim is weak? Can they spot when a statistic is outdated or being used out of context? Can they interview someone and hear the actual story beneath the corporate talking points? Can they understand why a founder’s opinion matters to one outlet but sounds self-promotional to another? Can they write differently for e27, Tech Collective, a lifestyle publication, a LinkedIn post and a client byline without flattening everything into the same generic tone? That is where talent now shows up.

Also Read: Is our talent pipeline ready for the AI economy? Not in the way we think

AI proficiency matters, but not in the way many people think

There is a temptation to treat “AI skills” as a new line item on a job description. Can the candidate use ChatGPT? Can they write prompts? Can they generate content faster? These are useful questions, but they are shallow on their own.

In content and media roles, AI proficiency should not mean the ability to outsource thinking to a tool. It should mean knowing how to use AI without losing editorial judgment. A strong candidate should be able to explain what they would use AI for, what they would never use it for and how they would verify the output.

For example, I would be more impressed by a candidate who says, “I use AI to test headline options and identify gaps in structure, but I do my own source checking and rewrite the argument myself,” than one who simply says, “I can produce five articles a day using AI.”

Speed is useful, but speed without discernment creates risk. In media and communications, that risk may appear as factual errors, bland thought leadership, weak attribution, cultural tone-deafness or content that sounds polished but says very little. For startups and agencies in APAC, where one article may need to work across Singapore, Malaysia, Indonesia, Vietnam or broader regional audiences, that lack of judgement can damage credibility quickly. The best talent today is not anti-AI. It is AI-literate and editorially accountable.

What I now look for in writers and content talent

The first thing I look for is curiosity. Not the performative kind, but the kind that shows up in the questions someone asks before they write. A good writer does not simply ask, “What is the word count?” They ask who the audience is, why this topic matters now, what has already been said, what the client or publication wants to avoid, what claim needs proof and what the reader should walk away understanding. In an AI-saturated content market, curiosity is a competitive advantage because it leads to better inputs. Better inputs still produce better work, whether AI is involved or not.

The second signal is taste. This is harder to teach than grammar. Taste is knowing when a sentence sounds too inflated, when an opening paragraph is dragging, when a quote is weak, when a headline is technically accurate but emotionally flat. It is what helps a writer avoid the generic “in today’s rapidly evolving landscape” style of content that AI tools produce so easily.

The third is accountability. I want to know whether someone feels responsible for the accuracy and usefulness of the work. This is especially important in journalism-adjacent roles, PR and thought leadership. A writer who cannot explain why they used a certain source, framed an argument in a certain way or removed a claim from a draft is not ready to operate independently.

The fourth is adaptability. The strongest content professionals are not locked into one format or one voice. They can write a founder byline, edit a client comment, turn a press release into a story, prepare interview questions, write a social caption and understand why each one requires a different approach. Finally, I look for perspective. AI can summarise what is already online. A strong writer can tell you what is missing from the conversation.

What matters less than it used to

This may be uncomfortable, but credentials matter less to me than they once did. A journalism degree, a communications qualification or a well-known previous employer can be useful signals, but they are not guarantees. Some of the strongest writers I have worked with were not the most credentialed. They were the ones who could listen carefully, think clearly and revise without ego.

Also Read: The creative gap: Why GenAI is outpacing the talent it was meant to empower

Years of experience also need to be examined more carefully. Someone may have spent five years producing content without ever learning how to shape an argument. Another person may have two years of experience but sharper editorial instincts, stronger research habits and a better grasp of digital audiences.

Even technical writing skill, while still important, is no longer the entire game. Grammar can be cleaned up. Structure can be improved. What is harder to fix is a lack of thinking. This does not mean lowering standards. It means raising them in the right places.

What this means for APAC’s HR and media ecosystem

Across APAC, companies are under pressure to produce more content, more quickly and across more channels. Startups need founder visibility. Tech companies need thought leadership. HR teams need employer branding. PR agencies need bylines, pitches, commentary and media-ready narratives. Publications need contributors who understand their audience and do not waste editorial time.

At the same time, budgets are tight, and AI tools are making leaders question what they should still pay humans to do. The answer is not to pay people merely to generate text. That work will continue to be automated, compressed or devalued. The answer is to pay people who can combine domain understanding, editorial judgement and strategic communication.

For HR leaders, this means rethinking how writing and content roles are assessed. Instead of asking candidates to produce a generic article from scratch, give them a messy brief. Ask them what they would question. Give them a weak AI-generated draft and ask them to improve it. Ask them to fact-check a paragraph. Ask them to explain which angle would work for which publication and why. In other words, test the thinking around the writing.

A practical framework for hiring content talent now

When hiring writers, editors, PR consultants or content strategists today, I would ask five questions.

• Can they think beyond the brief? A great hire does not simply execute instructions. They can identify what is missing, what is unclear and what needs to be challenged.
• Can they use AI without becoming dependent on it? The best candidates should be able to use tools for efficiency while still owning the final judgment.
• Can they adapt to the audience and context? A strong writer knows that a startup founder byline, a lifestyle feature and a regional tech analysis cannot sound the same.
• Can they handle feedback without losing the thread? In content work, revision is not a punishment. It is part of the job. Good talent can take feedback, improve the piece and still protect the core argument.
• Can they make the work more useful? This is the ultimate test. After they touch a draft, is it clearer, sharper, more accurate and more valuable to the reader?

Also Read: What hiring a high school graduate taught me about talent in the AI economy

The future belongs to writers who can think

AI has not made writing irrelevant. It has made average writing easier to produce. That distinction matters. For those of us who have built our careers on words, the shift can feel unsettling. But it is also clarifying. The market is no longer rewarding people simply because they can fill a page. It is rewarding those who can bring judgment, context, taste and responsibility to communication.

In media, PR, marketing and content roles, “great talent” no longer means the person who can write the cleanest first draft. It means the person who can understand what needs to be said, why it matters, who it is for and how to make it credible.

The tools will keep improving. More people will be able to produce acceptable content. But acceptable content is not the same as valuable communication. That is where good writers still matter. And that is why the best ones will still get paid.

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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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