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Every AI conversation seems to begin with the same question: what can AI do better than humans?

It is an understandable question since AI can now analyse information quickly, summarise long reports, generate first drafts, support customer service, and automate tasks that used to take hours. For many companies, the appeal is immediate. If a tool can help a team work faster, reduce repetitive work, and make better use of existing resources, it is difficult to ignore.

But I think there is another question we should be asking more often, especially in Southeast Asia: Who actually gets to benefit from this shift?

The current AI conversation often assumes that everyone starts from the same place. It assumes that workers have time to experiment with tools, businesses have budgets for training, and communities have equal access to digital infrastructure. In reality, the gap between those who are ready for AI and those who are not is still very visible.

This is where the discussion about human value becomes more interesting. The issue is not simply whether AI will replace certain tasks. It is whether we are building an AI economy where more people can meaningfully participate.

Human value is changing, but it is not disappearing

Much of the anxiety surrounding AI comes from the belief that machines are replacing human value. I understand where that concern comes from, but I do not think it tells the full story.

For a long time, many professional skills were built around access to information. People were valued for how quickly they could research, organise knowledge, analyse trends, or turn information into a useful output. Those skills still matter, but AI has changed the baseline. A first draft, a summary, or a basic analysis is no longer as difficult to produce as it once was. That does not mean human value has disappeared. It means the source of value is moving.

In an AI-enabled workplace, the people who stand out are often not the ones who can simply produce the most output. They are the ones who can ask better questions, understand context, make sound judgments, and connect technology to real human needs.

Also Read: The accordion effect: How AI follows the rhythm of expansion and compression

AI can generate a list of ideas, but it cannot always know which idea is right for a specific market, community, or moment. It can analyse patterns, but it does not carry the lived experience needed to understand why people behave the way they do. It can help optimise a process, but humans still need to decide what kind of outcome is worth optimising for.

This is why I do not see the future of work as a simple story of humans versus machines. It is more likely to become a story of who can use machines with enough judgment, empathy, and responsibility.

The real divide is access, not interest

In Southeast Asia, interest in AI is not the problem. Many people and businesses are curious about it. However, the harder question is whether they have the same opportunity to learn, test, and apply it.

The World Economic Forum’s Future of Jobs 2025 coverage on Southeast Asia notes that digital skills are becoming more important for companies across the region, but many employers still see significant gaps. Upskilling and reskilling are becoming priorities because the pace of change is already affecting what businesses need from their teams.

This matches what many of us are seeing on the ground. Larger companies can invest in AI tools, internal training, consultants, and structured experimentation. Smaller companies often have to make do with limited time, limited budget, and limited guidance.

For workers, the difference can be just as stark. Someone in a major city with strong internet access, an English-language education, and exposure to global tools may find it easier to learn AI. While a frontline worker, informal worker, or small business owner in a less connected area may not have the same starting point.

The risk is that AI becomes another layer of advantage for people and organisations that already have access to capital, infrastructure, and education.

Southeast Asia needs inclusive AI growth, not just faster AI adoption

The region’s digital economy is still growing quickly. The e-Conomy SEA 2025 report says Southeast Asia’s digital economy has grown from US$40 billion in GMV a decade ago to more than US$300 billion in 2025.

Indonesia is a useful example of why inclusion matters in this conversation. MDI Ventures’ recent white paper, Catalysing Digital Resilience and Sustainable Growth: Advancing Inclusive Innovation and AI-Driven Impact Across Indonesia’s Digital Economy, notes that the country has around 65 million MSMEs, contributing 60.5 per cent to GDP and absorbing 96.5 per cent of the national workforce. It also points out that Indonesia’s digital economy is projected to reach between US$180 billion and US$340 billion by 2030, while many small businesses still face challenges in financing access, digital infrastructure, cybersecurity, and AI readiness.

Also Read: Singapore, AI, and the rise of emotional outsourcing

That context matters because Indonesia’s digital economy cannot be considered truly strong if its smaller businesses are left behind. Growth may happen at the top, but resilience depends on whether the broader business ecosystem can participate.

This is where AI should be seen as more than a productivity tool. If applied well, it can support better credit scoring, improve access to digital financial services, strengthen cybersecurity, and help small businesses operate with more confidence. But these benefits will not spread automatically. They need infrastructure, trust, relevant products, and patient ecosystem-building.

The MDI white paper makes this point indirectly through its focus on impact capital, digital trust, AI, cybersecurity, and inclusive digital infrastructure. Its portfolio examples, including Amartha, Qoala, Privy, and CYFIRMA, show how technology can support access, protection, identity, and trust within the wider digital economy.

We should also think about how people learn

There is another part of this shift that deserves more attention. As companies automate more entry-level tasks, we may accidentally weaken the pathways that help people build experience.

Many junior roles are built on tasks that are not glamorous but are deeply educational. Writing meeting notes, preparing research, drafting reports, checking details, and supporting senior colleagues are often how people learn how an industry works. These tasks teach judgment slowly. They expose people to context, mistakes, client expectations, and decision-making.

If AI takes over too much of that early work without a replacement learning path, companies may solve one efficiency problem while creating a future talent problem.

This is why the talent conversation should not stop at whether people know how to use AI tools. The deeper question is how quickly people can keep learning as the nature of work changes. LinkedIn estimates that 70 per cent of the skills used in most jobs will change by 2030, while PwC’s 2025 Global AI Jobs Barometer found that workers with AI skills command a 56 per cent wage premium. This suggests that AI is not simply reducing the value of human talent. It is raising the value of people who can keep adapting.

For organisations, the risk is that workers who already have access to training, tools, and experimentation time will move further ahead, while those without that access fall behind. This does not mean companies should avoid automation. It means they need to be more intentional about learning.

If AI handles the first draft, junior employees still need to learn how to evaluate that draft. If AI summarises research, people still need to learn how to question the source, spot missing context, and decide what matters. If AI supports execution, teams still need to teach accountability, communication, and ethical judgment.

AI can speed up work, but it should not remove the process through which people become thoughtful professionals.

Great talent now looks different

This also changes what we should look for in talent. A few years ago, the strongest candidate might have been the person with the most polished technical skills or the most impressive credentials. Those things still have value, but they are no longer enough on their own.

Also Read: AI slop is a strategy problem, not a content problem

In an AI-enabled environment, I would pay closer attention to curiosity, adaptability, clarity of thinking, and the ability to work with ambiguity. I would also look for people who know how to use AI without outsourcing their judgment to it.

That last part matters. There is a difference between someone who uses AI to think better and someone who uses AI to avoid thinking. The first person becomes more capable. The second person becomes more dependent.

This is why AI literacy should not be treated as a narrow technical skill. It is becoming part of how people communicate, analyse, make decisions, and build trust. The strongest professionals will be those who can combine technological fluency with human understanding.

The future of AI should be measured by who gets included

Many businesses are asking how AI can help them do more with fewer people. That is a practical question, and it will not disappear.

But I hope more leaders also ask a broader question: how can AI help more people contribute?

That question leads to a different set of priorities. It pushes organisations to invest in training beyond senior teams. It encourages businesses to think about frontline workers, small merchants, regional entrepreneurs, and communities that may not be first in line for new technology.

Southeast Asia’s future growth will depend not only on how quickly AI is adopted, but on how widely its benefits are shared. If smaller businesses, young workers, and underserved communities are left behind, the digital economy may become more advanced without becoming more resilient. That would be a loss for everyone.

In the end, the most important human contribution in an AI-powered world may not be competing with machines. It may be making sure the future we build with them still works for more humans.

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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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Malaysia-based venture capital firm Vynn Capital has made a strategic investment in etaily, the Philippines-born commerce and retail infrastructure platform, as the company doubles down on Malaysia, Singapore, and Indonesia to build a regional operating layer for consumer brands selling across Southeast Asia.

The size of the investment was not disclosed.

Also Read: Ayala Ventures, Foxmont Capital join etaily’s US$1.6M seed round

The deal comes months after etaily raised a late-2025 financing round led by Sumitomo Mitsui Banking Corporation’s Asia Rising Fund. It also adds a Malaysian investor to a cap table that already includes Pavilion Capital, Ayala Corporation, the Gokongwei Group, the Cheng family behind Landmark, the Po family behind Century Pacific Food Corporation, Magsaysay family investors, Kaya Founders, Japan’s SBI ICCP Fund, and Foxmont Capital.

Founded in 2020, etaily helps consumer brands run and scale online and omnichannel operations across Southeast Asia. Its platform covers marketplace management, direct-to-consumer commerce, social commerce, livestreaming, retail media, customer experience, fulfilment coordination, data analytics, and offline retail enablement.

The company works with more than 100 brands, including L’Oréal, Levi’s, Skechers, Fila, Vans, Columbia, and The North Face.

Malaysia becomes a bigger piece of etaily’s regional plan

For etaily, the Vynn Capital investment is less about entering Malaysia and more about making the market a core pillar of its Southeast Asian cluster strategy. Over the past year, the company has expanded local operations, hired dedicated teams and secured regional commerce mandates for global brands through partnerships, including Gulf Marketing Group, one of the Middle East’s largest retail operators.

The company is building what it describes as a multi-country cluster across the Philippines, Malaysia, Singapore, and Indonesia, allowing brands to enter and manage multiple Southeast Asian markets through a single operating framework. Several enterprise brands, including Vans, The North Face, Columbia, and Timberland, have used etaily’s multi-country operations.

“Malaysia is becoming an increasingly important pillar within our Southeast Asia cluster strategy,” said Alexander Friedhoff, founder and CEO of etaily. “Having a partner like Vynn Capital is highly strategic for us given their deep understanding of logistics, operational infrastructure, and regional scaling dynamics.”

Vynn Capital, founded in 2018, invests across mobility, fintech, commerce, supply chain, property technology, food and consumer technology, and business enablement platforms. The firm is led by Victor Chua, Tunku Ali Redhauddin ibni Tuanku Muhriz and Darren Chua, and has built a regional network across Malaysia, Singapore, Indonesia and Thailand.

Also Read: The long and winding road to e-commerce profitability

For etaily, that network could matter as much as capital. Commerce enablement in Southeast Asia is not only a software problem. It involves country-specific marketplace rules, fulfilment partners, warehouse operations, tax structures, retail relationships, creator networks and last-mile delivery constraints.

Why commerce infrastructure is attracting capital

Southeast Asia’s e-commerce market is large, but fragmented. According to the e-Conomy SEA 2024 report by Google, Temasek and Bain & Company, the region’s e-commerce gross merchandise value reached about US$159 billion in 2024, making it the largest component of Southeast Asia’s digital economy.

But growth has become more complex. Brands are no longer selling through one or two marketplace storefronts. They are juggling Shopee, Lazada, TikTok Shop, brand.com sites, livestreaming, affiliate creators, retail media campaigns and offline retail partners. Consumer acquisition costs have risen, discount-led growth has become harder to sustain, and marketplaces are pushing brands to spend more on ads, content and fulfilment efficiency.

This is where companies such as etaily come in. Rather than acting only as an agency or marketplace operator, etaily is positioning itself as infrastructure for brands that want regional expansion without building full local teams in every market.

Its “online-first, offline-to-follow” model starts with digital channels and expands into physical retail once demand, data and category fit are validated. This approach is increasingly relevant in Southeast Asia, where online discovery and offline purchase still overlap heavily, particularly in beauty, fashion, footwear and consumer goods.

The rise of TikTok Shop has also changed the playbook. Social commerce is no longer a side channel in markets such as Indonesia, Thailand, Vietnam, the Philippines, and Malaysia. Brands now need content production, creator management, livestream operations and campaign analytics alongside traditional marketplace execution.

etaily said the fresh capital will support AI-enabled commerce operations, retail media capabilities, fulfilment integration, social commerce expansion and cross-border brand growth initiatives.

A crowded but expanding field

etaily is not alone in chasing this opportunity. Southeast Asia has produced several commerce enablement and brand operating platforms over the past decade.

Thailand-founded aCommerce has long served enterprise brands across e-commerce operations, fulfilment and performance marketing. Intrepid, another player, operates in six markets in Southeast Asia and works with major brands on marketplace and digital commerce execution. Singapore-linked Synagie built a regional e-commerce enablement business before being acquired. AnyMind Group, while broader in scope, has also expanded across creator commerce, D2C support, logistics and brand growth services.

Then there are technology-led players such as Anchanto, which provides SaaS for warehouse and order management, and marketplace-native tools that help sellers optimise listings, inventory and advertising. At the channel level, Shopee, Lazada and TikTok Shop are also deepening their own brand services, advertising products and fulfilment offerings.

This means etaily’s challenge is not just expansion, but differentiation. The company’s pitch rests on combining operational execution with data, AI, retail media, creator commerce, and offline retail enablement under one regional structure. If it can make that model work across Malaysia, Singapore, Indonesia, and the Philippines, it could become more than an outsourced e-commerce operator.

Philippines roots, regional ambitions

The investment also underlines a broader shift: more venture-backed companies from the Philippines are attempting to scale into Southeast Asia, rather than remaining domestic plays. etaily’s rise has been recognised by the Financial Times, which ranked it as the third-fastest-growing company in Asia Pacific in 2025 and the fastest-growing company in the Philippines.

Also Read: SEA e-commerce surges to US$185B as video commerce becomes the new growth engine

That ranking gives etaily momentum, but regional expansion will test whether its Philippine success can be replicated in more competitive and operationally demanding markets. Malaysia offers a useful bridge: it is digitally mature, connected to Singapore, influenced by regional retail groups and increasingly important for cross-border brand strategies.

Indonesia, however, will likely be the bigger prize and the harder test. It is Southeast Asia’s largest digital economy, but also one of the most complex, with intense marketplace competition, regulatory shifts around social commerce and highly localised consumer behaviour.

For Vynn Capital, the bet fits its focus on companies that modernise traditional industries through technology and operational infrastructure. For etaily, the investment gives it a stronger Malaysian anchor at a time when global brands are looking for fewer partners that can manage more markets, channels and customer journeys.

The next phase will show whether commerce enablement in Southeast Asia consolidates around a few regional infrastructure players — or remains a market of country specialists, agencies, logistics providers and marketplace-native operators stitched together by brands themselves.

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OMOWAY, a China-born intelligent mobility startup building smart electric motorcycles, has completed consecutive Series A and Series A+ financing rounds as it begins global deliveries of its flagship OMO-X model, starting with Indonesia.

The company said the Series A+ round was led by Lochpine Capital, an industrial investment fund backed by battery giant CATL. Its Series A round was led by Monolith, with CICC Capital and existing backer ZhenFund also participating.

OMOWAY did not disclose the exact amount raised, saying only that the two rounds brought in “tens of millions of US dollars”.

Also Read: Revolutionising urban commutes: Southeast Asia’s prominent electric two-wheeler startups

Other investors in the company include Hongshan, RocketsCapital, the corporate venture capital arm of XPeng, and Hui Capital, an industrial fund founded by a BYD co-founder.

Founded in July 2024, OMOWAY is positioning itself at the intersection of electric two-wheelers, robotics and connected mobility. Its first product, OMO-X, is marketed as a mass-produced self-balancing smart motorcycle built on the company’s proprietary OMO-ROBOT architecture.

The company has picked Indonesia as its first delivery market, a logical but highly competitive launchpad. Indonesia is one of the world’s largest motorcycle markets, with more than 120 million motorcycles on its roads, and two-wheelers remain the backbone of daily commuting, informal commerce and last-mile logistics across the archipelago.

Indonesia first, Southeast Asia next

OMOWAY said it began the first global customer deliveries of OMO-X in June, with Indonesia receiving the initial batch. The startup claims OMO-X became the top electric motorcycle brand in Indonesia by order volume during its launch month, although it did not disclose the number of units ordered or delivered.

The company has also set up dozens of dealer locations across Jakarta, Bandung, Surabaya, other parts of Java and Bali. After Indonesia, OMOWAY plans to expand deliveries to Thailand, Singapore, Europe and other markets.

The Southeast Asian angle is central to OMOWAY’s expansion story. Unlike Europe or the US, where electric cars dominate the electrification narrative, Southeast Asia’s transition is more likely to be led by motorcycles. In Indonesia, Vietnam, Thailand and the Philippines, two-wheelers are not niche vehicles; they are mass-market mobility infrastructure.

Indonesia has set ambitious goals to accelerate electric vehicle adoption, including targets for millions of electric motorcycles on the road by the end of the decade. Yet adoption has remained slower than policymakers hoped, held back by pricing, battery concerns, charging access, resale uncertainty and consumer loyalty to established petrol brands such as Honda and Yamaha.

Also Read: The real opportunity in ASEAN’s EV market lies in regional coordination

This is the gap OMOWAY is trying to enter: a market where the need is obvious, but where electric motorcycle makers still have to prove that they can offer not just lower running costs, but also reliability, service coverage and a better riding experience.

A crowded electric motorcycle field

OMOWAY is arriving in Indonesia at a time when the electric two-wheeler market is becoming increasingly crowded.

Local and regional players include Alva, backed by Indonesia’s Indika Energy; Polytron, which has pushed battery leasing models; Smoot, which has worked with battery-swapping infrastructure; and Gesits, one of Indonesia’s early domestic electric motorcycle brands. Singapore-headquartered ION Mobility is also targeting Indonesia with its M1-S electric scooter and has raised capital from investors including TVS Motor.

Beyond startups, Japanese incumbents remain the most formidable competitors. Honda and Yamaha have spent decades building dense dealership, financing and servicing networks across Southeast Asia. Their petrol motorcycles dominate roads from Jakarta to Ho Chi Minh City, and any meaningful shift to electric two-wheelers will require consumers to trust new brands on after-sales service, battery durability and spare parts availability.

China’s electric two-wheeler ecosystem is another competitive force. Chinese manufacturers have scale, supply-chain advantages and battery access, but they have also faced the challenge of adapting products to Southeast Asia’s road conditions, pricing expectations and regulatory requirements.

OMOWAY is attempting to differentiate through intelligence rather than price alone. The OMO-X comes with a digital key, a large smart navigation display and remote vehicle control. A higher-end Balance version adds low-speed balance assistance using the company’s self-balancing technology.

That feature is meant to address one of the oldest problems in motorcycling: instability at low speeds. In dense urban environments such as Jakarta, Bangkok and Ho Chi Minh City, where riders frequently crawl through traffic, stop suddenly or carry passengers and cargo, low-speed control can be more than a novelty. If the technology works reliably at scale, it could appeal to newer riders, delivery workers and urban commuters who want the convenience of a motorcycle without some of the intimidation that comes with handling one.

From motorcycle to wheeled robot

OMOWAY’s broader pitch goes beyond electric motorcycles. The company describes itself as a wheeled robotics company, not simply a vehicle manufacturer.

Its OMO-ROBOT architecture is designed as a closed-loop system integrating perception, decision-making, execution and information transmission. In practical terms, the company wants to turn two-wheelers into “two-wheeled robots” capable of sensing, computing and responding to riding conditions.

OMOWAY said it has also developed Mobility One, a fully self-developed wheeled robot platform, with a prototype expected to be unveiled later this year. The company sees potential applications beyond personal mobility, including logistics and public services.

That ambition mirrors a broader shift in mobility investing. Investors are increasingly looking beyond hardware margins and asking whether vehicle startups can build software-led platforms, recurring service revenue, fleet management tools or robotics capabilities. This is especially relevant in Southeast Asia, where last-mile delivery, ride-hailing and urban logistics remain large markets but are under pressure to reduce costs and emissions.

Still, execution will matter more than positioning. Building smart electric motorcycles at scale is difficult. So is maintaining dealer networks across fragmented island geographies such as Indonesia. The company will need to show that OMO-X can survive heat, humidity, rough roads, flooding, heavy usage and inconsistent charging access.

The road ahead

OMOWAY’s fresh funding gives it capital and strategic backing at a time when the electric two-wheeler market is shifting from early pilots to commercial competition. CATL-linked capital could also prove useful as battery supply, safety and cost remain key factors in the sector.

But Indonesia will be a demanding first test. Consumers in the market are value-conscious, petrol motorcycles are affordable and widely serviced, and electric alternatives still need stronger financing, charging and battery-swapping ecosystems to reach mass adoption.

For Southeast Asia, however, the stakes are significant. If electric two-wheelers can reach price parity, improve safety and integrate smarter software, they could play a major role in reducing urban emissions and fuel dependence across the region.

Also Read: Dat Bike teams up with Japan’s FCC in US$22M Series B round

OMOWAY’s first deliveries in Indonesia mark the beginning of that test. The company now has to prove that its “smart motorcycle” thesis is not just a technology story, but a commercially viable mobility business in one of the world’s toughest and most important two-wheeler markets.

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I’ve spent over a decade placing IT professionals across Vietnam’s tech landscape. I thought I’d seen every version of a talent war. Then healthtech arrived at scale, and it’s different.

The shortage isn’t in general developers. It’s at a very specific intersection: someone who can build robust software and understand why a clinician’s workflow looks the way it does. That profile is rarer than most hiring managers realise.

The market isn’t optional anymore

Vietnam’s digital health market is projected to reach US$815 million in 2026, growing to US$9.5 billion by 2034. But the more important story isn’t market size; it’s regulatory necessity.

Under Decision 749/QĐ-TTg, all healthcare facilities were mandated to implement EHR by 2025–2026. As of early 2026, approximately 1,210 of 1,650 hospitals have announced EMR implementations. The 2025 Decree on Health Data Management raised the bar further, requiring HL7/FHIR compliance, data encryption, audit logging, and digital signatures. The government has committed VND 30,000 billion (~US$1.26 billion) to back it.

This is compliance-driven demand. It doesn’t wait for organisations to be ready.

FDI is moving in lockstep: roughly 160 foreign projects in healthcare and pharmaceuticals with nearly US$1.8 billion in registered capital as of mid-2024. Every new foreign-backed healthtech venture needs the same thing, skilled IT professionals who can operate in a high-compliance, clinically-adjacent environment.

Four layers, four different hiring profiles

The mistake I see most often: treating “healthtech hiring” as one problem.

• Infrastructure (HIS/EHR): The most urgent layer. Compliance-aware engineers with HL7/FHIR fluency. Most non-negotiable demand, least glamorous work. FPT and VNPT dominate domestically, but international vendors are entering fast.
• Clinical AI: AI/ML Engineers with medical imaging experience (DICOM, radiology pipelines). Globally scarce — Vietnam is no exception.
• Telehealth and consumer: Product-minded mobile and backend engineers. Closest to fintech talent in profile, and therefore most contested.
• Healthcare commerce: High-transaction backend, supply chain architecture. BuyMed (US$51.5M Series B) has defined this layer’s standard.

Hiring “a backend developer for our healthtech project” without knowing which layer you’re building for is a mistake that shows up three months into onboarding.

Also Read: Vietnam isn’t just inviting private capital in. It is structurally dependent on it

The real gap

Vietnam produces 50,000–60,000 IT graduates per year across 153+ universities. The pipeline isn’t the problem. Average IT compensation has climbed ~35 per cent year-over-year, with senior developers commanding 50–70 per cent more than two years ago, signs of fierce competition, not scarcity of raw talent.

The problem is the intersection. Engineers with genuine clinical workflow understanding, HL7/FHIR experience, and compliance-aware development instincts are a small fraction of the market. According to the ITviec IT Salary and Recruitment Report 2025–2026, demand for digital transformation talent is surging across all sectors — healthtech is competing for the same pool as fintech, e-commerce, and enterprise SaaS, all at once.

In our pipeline, roles like Healthcare IT PM, medical imaging AI Engineer, or Health Data Security Specialist routinely take two to three months to fill through standard processes. Organisations that haven’t invested in employer branding for healthtech will lose candidates at the offer stage, consistently.

Speed is the strategy

The organisations winning right now have one thing in common: they treat hiring as a product problem, not an HR process.

EHR mandates, funding milestones, and product launches don’t accommodate 90-day recruitment cycles. The practical moves that work:

• Pre-built talent pipelines over job postings, the difference between a seven-day shortlist and a 70-day search is whether the candidate relationship already exists
• Staff augmentation to align headcount with project phases, not lock in fixed overhead at the wrong moment
• EOR structures for Singapore, Japanese, and Australian companies building Vietnam teams — bypassing the three-to six-month entity setup before a single hire is made

One overlooked angle: interoperability engineers. Vietnam’s EHR adoption numbers look strong, but hospitals are still storing data in incompatible formats, a problem the Vietnam Medical Informatics Association has flagged publicly. Engineers with HL7 FHIR integration experience and legacy system API skills are disproportionately valuable right now, before the next regulatory tightening cycle.

Also Read: Vietnam’s biggest PE bet of 2025 was not on tech. It was on what 100M people eat every day

The window is open (for now)

The regulatory mandate is real. The FDI is deployed. The government’s commitment is sustained. What’s uncertain is which organisations build the right teams before the talent market tightens further.

The ones that win won’t necessarily have the largest budgets. They’ll be the ones who understood early: in a compliance-driven, deadline-pressured market, hiring speed is a competitive advantage, and they structured their strategy accordingly.

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I’ve lived across Southeast Asia long enough to know what the heat feels like in five different cities.

Jakarta. Ho Chi Minh City. Singapore. Kuala Lumpur. Manila.

They all feel different. But they’re all on the same clock.

I’m not a climate scientist. I’m not a fund manager with a climate mandate. I’m someone who has spent over a decade moving across this region, watching capital flow, watching ventures rise and fold, watching ecosystems get built and ignored. I write about impact capital every week. But this week I want to write about something more personal.

I don’t have kids. My relationship with the future works differently from most people my age. It runs through two dogs, a meditation practice, and a stubborn, quiet belief that this region doesn’t have to end the way the projections currently suggest.

That’s why I keep watching climate when the room has moved on.

And right now the room has very much moved on.

The AI gravity problem

Every founder pitch I see has an AI angle. Every fund narrative has pivoted to include intelligence, automation, and agents. I get it. The returns are real. The narrative is loud. The FOMO is louder.

But here’s what’s getting drowned out.

Jakarta is sinking. Literally. Parts of North Jakarta have already dropped several metres, and the projection hasn’t changed. Ho Chi Minh City floods regularly now in ways it didn’t a decade ago. Manila’s coastal communities are being quietly relocated. Bangkok is dealing with saltwater intrusion. Singapore, the most climate-prepared city in the region, is spending billions on sea walls and still isn’t sure it’s enough.

This isn’t future risk. This is the current reality.

And yet. According to Tracxn data tracking SEA climate tech, funding in 2026 so far has recorded only four rounds totalling roughly US$17 million. That’s down nearly 60 per cent from the same period last year. Meanwhile, global AI funding crossed US$100 billion in the first half of 2025 alone.

The attention gap is real. And it’s widening.

Also Read: Funded: I keep a notebook by my bed with one question about SEA climate

The observer’s dilemma

I sit at the edge of the climate ecosystem. Not fully inside it. More like someone with their nose pressed against the glass, taking notes.

What I see from out here is a gap between urgency and attention. The urgency is accelerating. The attention is fragile and easily stolen by whatever narrative is loudest that quarter.

In 2021, it was crypto. In 2023, it was generative AI. In 2025, it was agents. Climate was supposed to have its moment in between. It did, briefly. Then the room moved again.

The venture building in climate didn’t move. They’re still here. Rice decarbonisation tackling one of SEA’s largest methane sources. Seaweed biostimulants are replacing chemical fertilisers across smallholder farms. Biochar carbon removal. Agrifood waste converted to sustainable fuels. Decentralised solar reaching communities the grid forgot. These aren’t concepts. They’re operating companies with revenue, with farmers, with real emissions reductions happening right now.

They just don’t trend.

The handful of funds that stayed committed to this space know this. SEEDS Capital, Entrepreneur First, East Ventures, SGInnovate and 100×100 formerly Wavemaker Impact, which just rebranded after spinning out as an independent fund manager with a fresh US$100 million mandate to build 50 climate companies across SEA and India, have done the unglamorous work of showing up round after round. Between them, they represent what conviction actually looks like in a space that doesn’t reward impatience.

Everyone else mostly came once.

What the future generation inherits

I think about this a lot. Not in a guilt-ridden way. More practical.

The cities I’ve lived in across this region are places people love. Street food at midnight. Communities that take care of each other. Chaos that somehow works. There is a version of 2040 where all of that is still here, adapted, resilient, figuring it out.

And there is another version.

Also Read: Investing in impact: High-growth tech for climate and community

The IPCC estimates that without significant intervention, Southeast Asia faces GDP losses of up to 11 per cent by 2100 due to climate impacts. The Asian Development Bank puts the region’s climate adaptation financing gap at over US$100 billion annually. Indonesia’s JETP commitment alone sits at US$21.6 billion. Vietnam’s at US$15.5 billion. The money being talked about is enormous.

The money actually reaching climate ventures at the early stage is not.

The capital deployed in the next five years will have more influence over which version of 2040 shows up than most people in the venture ecosystem currently acknowledge. That’s not an activist talking. That’s just what the data says when you read it without the AI hype in the background.

The ask isn’t to stop building AI

It’s to hold both.

The founders building climate ventures in SEA right now don’t need sympathy. They need capital that stays. They need fund managers who treat climate the same way they treat AI – as a structural bet on where the world is going, not a checkbox on an LP deck.

900 backers have put money into climate tech in SEA, according to recent data. You can count the ones who kept showing up on one hand.

The next generation doesn’t get to choose the cities they inherit. But the people reading this do get to choose where they put their attention and their capital right now.

I’m watching. Still at the window. But watching closely.

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Every project I’ve worked on that went over budget had one thing in common: the people paying for it thought they understood what they were buying. They had a quote, a timeline, and a feature list. What they didn’t have was a real picture of what drives cost in software development. And that gap is expensive. 

I’ve seen a six-figure project balloon because nobody mapped the third-party integrations before kickoff. I’ve seen a “simple” admin panel turn into a three-month ordeal because the access control requirements weren’t defined until week five. That’s exactly what can happen with your project if your cost planning stays surface-level. 

According to Radixweb, a software development company, project costs typically range from US$15,000 to over US$100,000, with the final number shaped by complexity, feature scope, tech stack, and integration requirements. That range exists because software cost isn’t a fixed thing; it’s the sum of hundreds of decisions, many of which get made casually and early. 

Here’s what actually moves the needle. 

The core factors that shape your development budget

Scope: where budgets go to die

Scope isn’t just a list of features. It’s the depth of each feature, the edge cases each one has to handle, and the integrations each one touches. A login screen is a login screen, until it needs MFA, social logins, SSO for enterprise clients, and role-based permissions. Now it’s a two-week job. 

What makes this dangerous isn’t that requirements grow. It’s that they grow quietly. A stakeholder adds something in a meeting. A developer makes an assumption. A “small change” gets absorbed without a conversation about what it costs. By the time anyone notices, the timeline has shifted and the budget is already stressed. 

Before any development begins, force a prioritisation conversation. Not “what do we want” but “what do we actually need at launch.” Every feature pushed to v2 is real money saved, and it’s almost always a feature you thought was essential until you asked the hard question. 

Team structure: You’re not just paying for hours

The sticker price of a developer rate is the least interesting cost question here. What actually matters is how your team is structured and how well it functions. 

A misaligned team (where the client, project manager, and developers are working from different assumptions) generates rework. Rework is expensive not just in hours, but in the momentum it kills. I’ve watched projects where the developers were sharp, and the hourly rate was fair, but the communication structure was so poor that the same features got rebuilt two and three times. 

When you’re evaluating a development partner, ask about their discovery and requirements process before you ask about their rate. A team that charges 20 per cent more but does a proper kickoff, documents requirements, and flags risks early will almost always be cheaper by the end. 

Also Read: The agentic shift: Why AI agents are rewriting the rules of ERP software in Singapore and Malaysia

Technology stack: Two costs, not one

People usually think about the tech stack in terms of build cost: what will it take to develop this? But there’s a second cost that hits you later: the operational cost of running what you built. 

Your infrastructure choices, your database architecture, your reliance on third-party APIs — all of which show up on a monthly bill once you’re live. A product built without scalability in mind might run fine at a few hundred users and require an expensive re-architecture at a few thousand. That’s not a hypothetical. It happens regularly, and it’s almost always preventable with the right conversations upfront. 

Pick a stack that has a healthy developer ecosystem (because you’ll need to hire or replace people eventually), that matches the operational demands of your product, and that your team actually knows well. Novelty is rarely worth the cost premium. 

The hidden costs that quietly break budgets

This is where I see the most financial damage, not in the obvious line items, but in the things nobody budgeted for because nobody mentioned them. 

Maintenance isn’t optional, it’s ongoing

The moment your software ships, the clock starts on its upkeep. Dependencies need updates. Security patches need to be applied. Browsers and operating systems change, and your product has to keep up. A rough but reliable rule: budget 15–20 per cent of your initial development cost every year for maintenance. If that number surprises you, the surprise is worse when it arrives unplanned. 

QA gets cut first and costs the most 

When timelines get tight, testing is usually the first thing squeezed. That decision consistently backfires. A bug caught in development costs a fraction of what it costs in production – in developer time, in user trust, and sometimes in legal exposure. A proper QA process isn’t overhead. It’s the thing that protects everything else you spent. 

Also Read: AI skills now translate into real pay gains for software engineers, NodeFlair finds

Integrations are underestimated almost universally 

Connecting your software to a CRM, payment gateway, ERP, or analytics platform takes longer than anyone expects, tests in ways that are genuinely hard to predict, and creates dependencies you’ll be maintaining forever. The more integrations your product needs, the more you should buffer your timeline and budget — not by 10 per cent, but meaningfully. 

Compliance is a technical cost, not just a legal one 

If your product touches personal data, health records, or financial information, frameworks like GDPR, HIPAA, or PCI DSS require specific technical controls. These aren’t checkboxes but features that need to be designed and built. According to the IBM Cost of a Data Breach Report, organisations that build security in from the start see significantly lower breach costs than those that treat it as a post-launch consideration. Retrofitting compliance after the fact is one of the most expensive things you can do in software. 

The decisions made in week one cost the most

Here’s the thing I wish more clients understood before we started working together: the most expensive part of building software isn’t the development. It’s features built on unclear requirements, architecture chosen for speed instead of longevity, integrations discovered after the fact, and bugs shipped because testing got cut. 

Every major cost overrun I’ve been close to was traceable to something that happened (or didn’t happen!) in the first two weeks. The practical answer is a real discovery phase. Before coding starts, map your requirements in detail, identify your integration points, flag your technical risks, and define what “done” actually means for each feature. It feels like slowing down. It’s actually the fastest path to a product that comes in on budget, because it’s the only way to know what you’re actually building before you’re paying to build it. 

Software development costs are not arbitrary. They are the accumulated result of decisions, some deliberate, many not. Get serious about the decisions, and the costs take care of themselves. 

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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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The post What actually drives software development costs (and why most budgets get it wrong) appeared first on e27.

CME Group recently announced plans to launch Compute Futures, tied to GPU and AI compute capacity. At first glance, this feels counterintuitive. Compute is a technology-driven input where costs consistently decline over time due to hardware improvements, manufacturing scale, and efficiency gains. If the long-run direction is structurally downward, what is there to hedge or price in a futures market?

The key misunderstanding is assuming futures markets exist to express long-term price direction. In reality, they exist to manage short- to medium-term uncertainty, typically within a three- to 24-month horizon, the exact window where real-world capital allocation decisions are made.

This is why even structurally deflationary commodities such as crude oil, natural gas, DRAM, and solar modules still have deep and liquid futures markets. Their long-term cost curves may trend downward, but their short-term prices are driven by highly volatile factors: supply chain disruptions, capacity constraints, inventory cycles, and demand shocks. Market participants are not hedging the fact that something becomes cheaper over decades; they are hedging whether it becomes more expensive or scarce over the next operating cycle.

The same logic applies to compute. For AI labs, hyperscalers, and enterprise users, the relevant risk is not GPU prices in 10 years, but the cost of training runs, inference capacity, and cluster usage in the next quarter or fiscal year. Compute Futures allow these participants to lock in a forward price for compute capacity, converting a variable input cost into a fixed, predictable operating expense.

Also Read: 15 Southeast Asian semiconductor startups moving beyond assembly

This also reflects a structural shift in what compute actually is. Compute is no longer purely a capital good like a CPU or server. It is increasingly a consumable infrastructure service, closer to electricity, airline seats, or hotel rooms. These markets share a critical property: non-storability. An unused GPU-hour cannot be saved for later use, just as an empty hotel room or unsold airline seat has zero value once the time window passes.

Because of this, even if GPU hardware continues a long-term deflationary trajectory, compute rental prices can still exhibit sharp short-term volatility. The constraints are not just chip prices, but system-level bottlenecks: data centre construction cycles (often 18 to 36 months), power grid availability, cooling infrastructure, and uneven deployment of GPU capacity.

On the demand side, volatility is amplified by AI-specific cycles: model breakthroughs, hyperscaler capex waves, startup funding cycles, and sudden surges in inference demand. These factors create mismatches between supply and demand that can push compute prices sharply higher or lower in short periods, independent of hardware cost trends.

Conclusion

Compute Futures are not a bet against long-term price decline. They are a response to short-term price instability in a rapidly scaling AI infrastructure market. As compute becomes a core production input in the AI economy, financial markets are beginning to treat it less like technology hardware and more like a tradable infrastructure commodity with its own risk management and pricing system.

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People used to ask AI for help with parts of modern life, like making an email sound less annoyed or explaining a spreadsheet formula without forcing anyone to revisit their relationship with mathematics. Lately, the exchange has become more intimate. The same tools built to summarise, draft and optimise are now being invited into moments of doubt, stress and loneliness.

This shift is global, but Singapore gives us a useful early signal. In my work with individuals and organisations in Singapore, I often see how much emotional load people carry while still functioning. Many keep moving through demanding workdays, family responsibilities and social expectations while privately trying to make sense of what they feel.

AI is starting to enter that private space.

A recent Singapore-based study offers a useful glimpse of how this is already happening. In 2026, researchers explored how foreign domestic workers in Singapore used a large language model chatbot while managing caregiving burden. The findings were cautious, but revealing. Participants described the chatbot as emotionally validating, psychologically safe, linguistically accessible, and useful for reassurance and companionship.

For a startup audience, this should raise more than a social welfare eyebrow.

The study points to a wider behaviour change that founders, product teams and employers need to understand. People are beginning to use general-purpose AI tools for emotional processing, especially when human support feels too slow, expensive, risky, or socially complicated. The user may begin by asking for help with a message. Within a few minutes, they may be asking whether their reaction makes sense, how to handle a difficult conversation, or why they feel so depleted.

That is where product design crosses into psychology and the appeal is clear. An AI can respond in plain language, adapt to imperfect phrasing, and give people a feeling of being heard without the social exposure that often comes with disclosure. For people under pressure, that can be powerful.

Also Read: How centralised exchanges swapped crypto ethos for Wall Street fees: Why this will fail

A 2026 cross-cultural study of more than 4,600 participants across seven countries found that people are already using large language models as always-available, non-judgmental confidants for emotional support. The prompts collected in that study showed people seeking help for loneliness, stress, relationship conflict and mental health struggles. This is no longer a fringe use case for companion apps. It is becoming part of ordinary interaction with general-purpose AI.

That shift has real commercial relevance.

If people are using AI tools to manage emotional load, then workplace software, productivity platforms, coaching apps, HR tools and digital health products are already operating closer to mental health territory than many companies may realise. A product designed to help someone draft a message can quickly become a place where they disclose fear, resentment, shame or distress. A tool designed to improve productivity can become the place where an employee admits they are no longer coping.

This creates opportunity, but also responsibility.

Emotionally responsive AI can reduce friction. It can help people name what they are experiencing, organise their thoughts and access support earlier. In a place like Singapore, where people may be managing long hours, family responsibilities, cultural expectations and pressure to remain composed, a low-barrier tool can feel useful. For employers and founders, that usefulness is exactly why the ethical design questions cannot be left until later.

Singapore gives this global shift a sharper local frame. In April 2026, NTU Singapore and NHG Health announced ASPIRE, Singapore’s first work-study training pathway for clinical psychology. The announcement pointed to a clear pressure point: demand for mental health support is rising, while the human workforce takes time to build. That is the gap AI is already moving into.

There is also a trust issue here.

People disclose differently when they believe no human is listening. They may share sensitive details with AI because the interaction feels contained, even when the data environment is more complex than it appears. For companies building emotionally fluent products, privacy cannot sit buried in compliance language. It has to be visible in the user experience. People need to understand what they are sharing, where it goes, how it may be used, and what the tool can do when distress escalates.

Also Read: The death of the traditional org chart: How AI is reshaping work

The most important lesson for startups is that emotional support may appear inside products that were never designed for mental health. A person may stumble into it while drafting a resignation email, preparing for a performance review, translating a difficult message, or trying to make sense of workplace tension. The product team may think they are building a writing assistant. The user may experience it as the first place they can say what they are really feeling.

That is where the next stage of AI design needs more psychological literacy.

Emotionally responsive tools should help people reflect, clarify and access support earlier. They should also make their limits clear. When a user starts disclosing distress, the product needs thoughtful guardrails: clear privacy language, careful emotional tone, referral pathways, escalation options and design choices that encourage agency rather than dependence.

Singapore’s 2026 research gives us an early signal of where this is heading. The study focused on foreign domestic workers using an AI chatbot for caregiving burden, but the lesson reaches further than that setting. People are turning to AI because it is immediate, private and easier to approach than many human systems of support.

For founders and organisations, the takeaway is simple: once a product becomes emotionally useful, it carries emotional responsibility.

AI is no longer only answering prompts. It is becoming part of how people process pressure, uncertainty and loneliness. The companies that understand this early will design tools that earn trust, protect users and know when to guide people back towards human support.

That is the next frontier of AI emotional support. The question is no longer whether people will bring their distress into the interface. They already are. The real design challenge is what the interface does with it.

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