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. 

—

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.

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.

—

The post Why Compute Futures make sense even in a deflationary market appeared first on e27.

The moment we are asked to implement while still experimenting, something within starts to strain.

Lately, I have been noticing this across organisations and teams working through AI integration. There is still a lot of curiosity. New tools, new ideas, new ways of working. At the same time, there is a very real shift happening underneath it.

Conversations are moving from what is possible to what is actually delivering value. The energy has not gone away, but the expectations have changed.

This same pattern has shown up in every major shift in technology

If you look back, the internet followed this exact rhythm. In the early days, it was about presence. Build a website. Try things. See what sticks. Over time, that shifted to performance. E-commerce, conversion, measurable outcomes.

Social media followed a similar path. Brands experimented with voice, content, and engagement. There was freedom in not knowing what would work. Then the shift came. Metrics tightened. Budgets followed performance. Creativity gave way to accountability.

SaaS created another version of this cycle. Organisations adopted tools quickly, often faster than they could integrate them. Over time, the conversation changed from access to utilisation. Are we actually using what we are paying for? Are these tools driving efficiency or just adding complexity?

Cloud was no different. The early push was migration. Move everything. Modernise. Then came the next phase. Cost control. Optimisation. Making sure the investment delivered real operational value.

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

AI is following the same pattern, just at a faster pace

There is a period of expansion where experimentation takes the lead. Organisations explore, build, and test what could work. Over time, that expansion gives way to compression, where the focus turns to implementation, execution, and scale.

The goal is no longer discovery, it is impact.

When organisations move on from experimentation before fully implementing, they leave value on the table and dilute the return on their investment. Right now, many organisations are sitting in between those two states, and in professional services, this tension is even more pronounced.

The people who would benefit most from the efficiency AI can create are often the ones with the least amount of time to engage with it. They are delivering, managing clients, and keeping momentum. Their days are already full. Adding new tools and new ways of working on top of that does not create transformation. It creates strain.

There is also a mismatch happening with clients. Expectations for delivery remain high, often unchanged, while internal teams are being asked to rethink how the work gets done. That gap creates pressure that does not always get acknowledged.

At the same time, we are seeing more adoption happening at senior levels of organisations because they have more space to step back and engage with what is new. They have the ability to explore, test, and think more broadly about applications.

That creates a gap between where AI is being explored and where it actually needs to be implemented.

Junior teams, the ones closest to execution, are often operating in a different reality. They are focused on output, timelines, and immediate deliverables. Without space to experiment, the tools never fully integrate into how the work gets done.

This is where organisations begin to stall

Leadership is pushing for results. Teams are trying to keep up with existing demands. The shift from experimentation to implementation gets stuck in the middle.

There is a natural rhythm at play between expansion and compression. Expansion thrives on curiosity and openness. It invites exploration and new thinking. Compression requires focus, clarity, and space to execute. It demands prioritisation and discipline.

Both are necessary. But they cannot be forced to happen at the same time in the same way.

Also Read: The AI layoff trap points straight at Southeast Asia

As leaders, our role is to recognise where we are in that cycle

Not where we want to be or where the market says we should be, but where we actually are in how our teams operate and what they’re being asked to deliver.

Three reflections for leaders navigating this shift:

• Define where value should show up: Not every experiment needs to scale. Be clear on where implementation matters most and focus your energy there. This creates direction in a moment that can easily feel scattered.
• Create space for change to take hold: If teams are fully consumed by delivery, new ways of working will not stick. Capacity is part of the work. This might mean some hard conversations with clients to reset expectations or reallocate effort.
• Support the shift in how work gets done: Tools alone won’t change outcomes. Adoption requires new habits, new expectations, and time to integrate both. Without that, the tools remain separate from the work instead of improving it.

The movement between expansion and compression is constant. It does not stop with one wave of technology, and it does not resolve all at once. Each new cycle brings the same opportunity and the same risk.

Recognising where you are within it and adjusting how you lead accordingly is what allows progress to take hold in a way that lasts.

This article was co-written with TJ Kelly, a senior partner at Penta Group.

—

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.

The post The accordion effect: How AI follows the rhythm of expansion and compression appeared first on e27.

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.

—

The post Singapore, AI, and the rise of emotional outsourcing appeared first on e27.

The post The Independence Day crypto puzzle: Up or down? appeared first on e27.

Few ideas in product management have travelled as widely, and done as much quiet damage, as the line that the product manager is the CEO of the product.

It sounds flattering. It sounds decisive. It gives the role a sense of weight and status that many product managers, especially early in their careers, are understandably drawn to. It also gives founders and executives a convenient shorthand. Say it once, and everyone feels they understand what product management is supposed to be.

The trouble is that they usually understand the wrong thing.

The phrase survives because it gives emotional clarity in a role that is often structurally ambiguous. Product managers sit in the middle of competing demands, partial authority, and shifting expectations. Telling them they are the CEO of the product feels empowering. It appears to solve the identity problem. It suggests ownership, leadership, and accountability in one neat line.

But neat lines are often expensive in real organisations.

Product leadership is not executive sovereignty

The first issue is simple. A CEO has formal authority. A product manager usually does not.

That is not a trivial difference. It is the difference.

A CEO can allocate capital, set structure, hire leaders, make final trade-offs across functions, and carry formal accountability for enterprise outcomes. A PM does not sit in that position. A PM works through influence, judgement, framing, trust, and alignment across people who often report elsewhere and carry valid goals of their own.

Pretending those are the same kind of leadership is not empowering. It is misleading.

In fact, one of the core disciplines of strong product management is learning how to lead without the fantasy of unilateral control. That is not a lesser form of leadership. In many ways, it is a more demanding one. It requires a PM to earn movement through clarity and substance rather than title. It requires them to understand technical constraints, business context, customer reality, and organisational incentives well enough to help a group arrive at better decisions together.

That is real leadership. It just is not CEO leadership.

The analogy encourages PMs to reach for authority they do not actually hold, instead of helping them master the kind of authority the role genuinely requires.

Also Read: The systemic minimum effective dose: Redesigning productivity through precision

The myth creates bad PM behaviour

Once people internalise the CEO idea, their behaviour often shifts in subtle but damaging ways.

Some start treating every decision as if the product should be the final arbiter. They become overly attached to control. They mistake coordination for command. They expect engineering, design, data, and go-to-market teams to line up behind product as though product were the natural centre of gravity in every discussion.

That posture creates tension quickly.

Engineering starts feeling managed rather than partnered with. Design feels invited in after the real thinking is done. Commercial teams learn that the product wants accountability without always carrying enough of the customer and market burden. The PM, meanwhile, often becomes more performative than effective. They begin signalling certainty, weight, and strategic dominance when what the situation actually needs is sharper listening, better synthesis, and more honest trade-offs.

It also creates bad organisational expectations

The damage is not limited to PMs themselves. The phrase also teaches the rest of the company to expect the wrong things from the product.

Executives start assuming PMs can simply make difficult trade-offs happen, even when the underlying functions are misaligned. Founders expect product managers to absorb accountability for outcomes without giving them enough organisational leverage to shape those outcomes properly. Engineers begin to resent the product for behaving like management without carrying equivalent depth in technical delivery. Customer-facing teams assume PMs should absorb every strategic tension because the role has been framed as the owner of the whole thing.

This creates a peculiar trap.

The PM is treated as highly accountable, but not always meaningfully empowered. They are expected to think like a general manager, influence like a founder, decide like an executive, and still somehow remain collaborative, humble, data informed, customer centric, and delivery conscious.

Also Read: People don’t want productivity hacks anymore, they want sustainable ways to live

The phrase flatters the product and diminishes everyone else

There is another problem with the CEO analogy that product people do not always say out loud. It quietly reduces the contribution of other disciplines.

If the PM is the CEO of the product, what exactly does that make the engineering lead, the designer, the data lead, the researcher, or the operational teams who deal with adoption reality every day? Supporting cast. Functional experts. Advisers to the central brain.

That is not how good products are built.

Strong products emerge from serious cross-functional thinking, where each discipline shapes the outcome in material ways. Engineering does not merely execute a product idea. It often determines what is elegant, resilient, scalable, and even strategically possible. Design is not visual packaging around a product direction. It shapes behaviour, trust, comprehension, and flow. Research does not just validate. It often reveals that the original framing was weak. Commercial teams do not only distribute value. They expose whether the product meets reality outside internal narratives.

The role is closer to an integrator than a sovereign

If the CEO analogy is wrong, what is a better frame?

In my view, a PM is far closer to an integrator of decision quality than a sovereign owner of the product.

That may sound less glamorous, but it is actually more accurate and, in mature organisations, more powerful.

A good PM helps the company make better product decisions by integrating customer truth, business judgement, delivery reality, and strategic intent. They create coherence where functions would otherwise optimise locally. They help teams decide what matters, what trade-offs are real, what assumptions are weak, and what evidence should change the course.

That is not a small role. In fact, it is a very consequential one.

Also Read: When execution is free, the brief becomes the product

Why younger PMs are particularly harmed by this myth

The CEO phrase does particular damage early in a PM’s career because it teaches the wrong aspiration.

Instead of learning how to ask better questions, build trust across functions, reason through trade offs, and develop genuine taste in product judgement, many younger PMs end up performing seniority. They overfocus on status, decisiveness, and visible ownership. They try to sound like mini executives before they have learned how to become truly useful in complex product environments.

This leads to a familiar pattern. The PM talks strategy when the team needs clarity. They push for alignment without understanding why disagreement exists. They seek authority before they have built enough credibility. They chase the optics of leadership rather than the substance of it.

The irony is that the best PMs often look less like product CEOs and more like unusually effective interpreters of complexity. They are calm under ambiguity. They know when to push and when to absorb. They improve decision quality without needing to dominate every room. They understand that influence is not diluted by collaboration. It is often made stronger by it.

Those are harder lessons to learn if the profession keeps telling people the goal is to act like a CEO.

—

The post The problem with ‘PM as CEO of the Product’: A myth that hurts more than helps appeared first on e27.

MAKATI CITY, Philippines — Sprout Solutions, the Philippines’ leading homegrown HR and payroll technology company, will hold the State of HR Summit 2026 on July 8, 2026, at Dusit Thani Manila, bringing together HR leaders, business executives, and people managers to discuss how organisations can accelerate performance with AI while remaining people-first.

Presented in partnership with workplace consultancy BS Works, this year’s summit carries the theme Leading a People-First AI Transformation and will feature the exclusive launch of the State of HR 2026 Report, based on findings from a nationwide survey of 3,516 Filipino professionals.

As AI becomes increasingly embedded in business operations, organisations face a new challenge: ensuring technological transformation strengthens employee trust, engagement, and readiness rather than undermining them. The summit will focus on how leaders can navigate this shift by aligning people, processes, and technology.

The discussion is grounded in findings from Sprout and BS Works’ nationwide AI and Your Evolving Workplace survey, conducted in May 2026 through Sprout’s first AI-powered survey application.

Early results reveal that 95 per cent of Filipino professionals are familiar with AI, while 89 per cent welcome AI-related changes in the workplace. However, organisational readiness appears to lag behind employee enthusiasm. Only 35 per cent of respondents report that their employers currently provide AI-related upskilling opportunities.

From AI adoption to AI leadership

The summit will open with remarks from Patrick Gentry, CEO and Co-Founder of Sprout Solutions. Cliff Eala, Managing Partner of BS Works, will deliver the keynote presentation and formally launch the State of HR 2026 Report, sharing insights on how employees are adapting to AI-driven workplace change.

Throughout the day, executive panels and discussions will explore the growing need to align HR, operations, and compliance functions, and the leadership capabilities required to guide organisations through rapid technological change. 

Among a host of industry leaders joining the summit are Maria Lourdes Ann “L.A.” Cruz, Vice President for People at Lufthansa Technik Philippines; Alvanson So, former HR leader at Canva; Eli Harell, Co-Founder of EmergePH; and Atty. Arlene De Castro, Founder and Managing Partner of De Castro Law Firm and De Castro Consulting. 

Attendees will also receive a preview of Sprout’s product roadmap, highlighting how the company’s people-first AI platform aims to connect payroll, compliance, performance, and workforce data within a unified ecosystem.

The readiness gap behind AI optimism

Additional survey findings highlight the urgency of the conversation. While 84 per cent of respondents report that their organisations already use AI and 83 per cent say AI helps them work more efficiently, nearly 45 per cent feel overwhelmed by the pace of workplace change.

Also Read: AI transformation starts with people, not platforms

“Filipinos have embraced AI faster than many organisations anticipated. The challenge now is helping leaders close the gap between adoption and readiness by building trust, developing skills, and creating workplaces where people can thrive alongside technology,” said Kislay Chandra, Chief Operations Officer, Sprout Solutions.

The event’s sponsors include mWell by Metro Pacific Health Tech as Platinum Sponsor, Hubstaff and MetroMart Philippines as Gold Sponsors, and Hive Health as Silver Sponsor. Supporting the initiative as Strategic Partners are Philippine Society for Talent Development, Mind You, and Plan International Pilipinas, while Rappler, Manila Standard, The Manila Times, Vritimes, TechShake, and Malaya Business Insight serve as Media Partners.

Recognising outstanding organisations through the foresight awards

The State of HR Summit 2026 will also host the inaugural Sprout Foresight Awards, recognising organisations demonstrating workforce health and people-first workplace practices.

Major awardees will be honoured through the Great Employer Awards, alongside other recognitions that celebrate excellence across key areas of organisational health. Through the Foresight Awards, Sprout aims to spotlight companies that are helping shape healthier, more resilient, and future-ready workplaces in the Philippines.

Powered by Foresight, Sprout’s new AI-powered predictive and prescriptive HR analytics solution, the awards reflect Sprout’s commitment to helping organisations better understand their workforce through meaningful data and actionable insights. 

Secure your seat at State of HR 2026

Registration for the State of HR Summit 2026 is now open at sohr.sprout.ph. Individual and table packages are available, and interested participants may visit the website for current rates and event details.

About the survey

The AI and Your Evolving Workplace survey was conducted by Sprout Solutions in partnership with BS Works and fielded nationwide in May 2026 through Sprout’s first AI-powered survey application. The study gathered responses from 3,516 professionals across major Philippine industries, including information and communication, business process outsourcing, finance, retail, healthcare, and manufacturing, representing multiple generations in the workforce.

About Sprout Solutions

Sprout Solutions is the people-first AI platform for payroll, compliance, and work, serving businesses across the Philippines and Southeast Asia. Founded in 2015, Sprout combines AI-powered payroll, HR, recruitment, performance management, employee engagement, and financial wellness solutions into one intelligent platform designed to help organisations operate more efficiently while supporting their people at scale.

Trusted by more than 2,000 companies and 350,000 users, Sprout complements its technology with managed services, implementation expertise, and dedicated customer success teams.

Sprout exists to impact the lives of employees by improving businesses in the communities it serves.

—

Want updates like this delivered directly? Join our WhatsApp channel and stay in the loop.

The e27 team produced this article in partnership with Sprout Solutions Phil., Inc.

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Featured Image Credit: Sprout Solutions Phil., Inc.

The post Sprout Solutions’ State of HR Summit 2026 focused on people-first AI transformation appeared first on e27.

Singapore-based dConstruct Technologies has closed a US$125 million Series A round, giving the city-state’s robotics sector one of its more visible funding wins at a time when deeptech companies across Southeast Asia face tougher scrutiny on commercial traction.

The National Robotics Programme of Singapore announced the financing on Thursday, positioning it as the standout outcome from the inaugural cohort of RoboNexus, its venture-building accelerator. The programme also highlighted two other graduates, LionsBot and Spinoff Robotics, as examples of Singapore robotics companies moving from research and pilot deployments into international markets.

Also Read: Thailand’s Amity Robotics raises US$7M to take physical AI from malls to global markets

The funding round places dConstruct among a relatively small group of regional robotics companies able to raise large institutional cheques. The company did not disclose the investors in the round, the valuation, or the breakdown between primary and secondary capital.

That lack of detail matters. Robotics companies tend to be capital-intensive, with long development cycles, massive hardware costs, integration work, and slower enterprise procurement timelines. Still, the size of the round suggests that investors are willing to back robotics platforms that can show deployment potential beyond narrow proof-of-concept projects.

Building for messy, indoor environments

dConstruct’s core technology is d.ASH, a proprietary suite that integrates 3D scanning and perception technologies to enable autonomous robots to operate in complex, GPS-denied environments.

That focus is commercially significant. Many real-world robotics applications inside buildings, tunnels, transport hubs, industrial sites, and underground infrastructure cannot rely on GPS. Robots operating in these settings need localisation, mapping, obstacle avoidance, and data capture systems that can function in cluttered, changing environments.

The company is targeting use cases across the built environment, security, inspection, logistics, and entertainment. Its disclosed customers and partners include Boustead Projects, Singapore’s Defence Science and Technology Agency, JRE Ventures of the East Japan Railway Company Group, SBS Transit, and SoftBank Robotics Singapore.

Chinn Lim, CEO of dConstruct Technologies, said RoboNexus had helped the company access industry partners and deployment opportunities, while the funding milestone reflected confidence in its plan to build “reality capture and robot automation solutions from Singapore to the world”.

The phrase is ambitious, but the underlying market is real. The International Federation of Robotics said global sales of professional service robots grew 30 per cent in 2023, with logistics, cleaning, inspection, and hospitality among key categories. Asia remains the centre of gravity for robotics adoption: the federation’s industrial robot data shows Asia accounted for about 70 per cent of new industrial robot installations globally.

Also Read: German-listed DDB acquires Singapore’s Infinium Robotics in US$24M share deal

Singapore has an additional advantage. Its small labour pool, high labour costs, ageing workforce, and dense urban infrastructure make it a natural testbed for automation. The country also ranks among the world’s most robot-dense economies, with the International Federation of Robotics placing Singapore near the top globally for industrial robot density.

A new robotics base in Punggol

dConstruct is also establishing a 42,000-square-metre global headquarters at Punggol Digital District. The facility, named dC Plus, is expected to be completed by the end of 2026.

The site will include robotics testing environments for wheeled, quadruped, and humanoid platforms, as well as collaborative workspaces and community programmes. The location is notable: Punggol Digital District has been positioned by Singapore as a hub for digital economy firms, applied research, and industry-academia collaboration.

For dConstruct, the headquarters could help address one of the sector’s persistent bottlenecks: moving from controlled demonstrations to repeatable deployment in real-world settings. Robotics companies often struggle not because their systems fail in the lab, but because customers need machines that can operate safely and reliably across unpredictable sites.

That challenge is especially acute in Southeast Asia, where built environments vary widely across markets. A robot that performs well in Singapore’s regulated commercial facilities may face different conditions in Indonesia’s logistics sites, Thailand’s factories, Vietnam’s industrial parks, or the Philippines’ mixed-use developments.

RoboNexus’s broader cohort

The National Robotics Programme also pointed to LionsBot and Spinoff Robotics as evidence that the RoboNexus model can support companies beyond early research commercialisation.

LionsBot, a Singapore-based cleaning robotics company, is now present in more than 40 countries and has overseas subsidiaries in Dallas, Amsterdam, and Chennai. The company says it has deployed more than 6,000 robots globally. Its expansion has been supported by a collaboration with WISAG, a European facility management group, which contributed to the development of LionsBot’s R5 cleaning robot launched in April 2026.

Cleaning robots are among the more mature categories in professional service robotics, but competition is intense. LionsBot faces global and regional rivals including Gaussian Robotics, SoftBank Robotics, Pudu Robotics, and Singapore-based players such as Sesto Robotics and OTSAW in adjacent automation segments. The differentiator is less about whether a robot can clean a floor and more about fleet management, maintenance economics, procurement relationships, and customer support across markets.

Dylan Ng, CEO and co-founder of LionsBot, said the company intends to support newer RoboNexus cohorts by sharing lessons from R&D, product development, commercialisation, and international expansion. That is useful if it moves beyond founder storytelling and into practical guidance on manufacturing, compliance, servicing, and channel partnerships.

Spinoff Robotics, meanwhile, has taken a different route. The company was acquired by Nanoveu Limited, an Australian Securities Exchange-listed technology company, giving it access to public-market capital, commercial platforms, and international growth channels.

The company was established to commercialise tethered aerial robotics technology for cleaning and inspection of hard-to-reach infrastructure. Its applications include urban infrastructure, facilities management, and industrial inspection. The acquisition also allows Spinoff Robotics to expand its licensed Singapore University of Technology and Design platform into non-tethered drone applications.

Also Read: AMC Robotics to build US$3.5M Vietnam factory as SEA automation race heats up

Tan Chee How, co-founder of Spinoff Robotics, said the National Robotics Programme helped the company move from research into commercialisation by opening access to industry partnerships and deployment opportunities.

Why this matters for Southeast Asia

For Southeast Asia, the story is not simply that a Singapore startup has raised a large round. The more important question is whether robotics companies in the region can build commercially durable businesses rather than remain grant-supported engineering projects.

Demand is not the problem. Southeast Asia’s factories, airports, ports, hospitals, malls, construction sites, and transport operators all face labour, safety, and productivity pressures. Governments across the region are also pushing automation through industrial upgrading programmes, including Singapore’s Advanced Manufacturing initiatives, Malaysia’s Industry4WRD policy, and Thailand’s Eastern Economic Corridor strategy.

The harder issue is deployment economics. Robotics companies must prove that their systems reduce labour dependency, improve safety, or generate operational savings large enough to justify upfront costs, integration work, and ongoing maintenance. Customers are no longer impressed by pilot projects alone.

dConstruct’s US$125 million Series A gives it the capital to build for that test. RoboNexus gives Singapore a stronger narrative around robotics venture-building. But the next stage will be less about national ambition and more about commercial evidence: repeat customers, overseas revenue, functioning fleets, and technology that survives outside carefully managed demo environments.

The post Singapore’s dConstruct lands US$125M Series A to scale robotics for GPS-denied environments appeared first on e27.

Omoway arrives in Southeast Asia with a compelling origin story: BYD co-founder as an investor, a fresh funding round, and a market that looks, on paper, ripe for disruption. Two-wheelers dominate urban mobility across the region. Fuel costs sting. Air quality in cities like Jakarta and Ho Chi Minh City has made clean transport a political talking point. The conditions seem ideal.

But the electric motorcycle race in SEA has already claimed confident entrants. Gojek-backed Electrum has struggled to scale beyond Indonesia. Vmoto, Gogoro, and a clutch of domestic players are all competing for the same cost-sensitive, range-anxious rider. The graveyard of well-funded EV two-wheeler startups that underestimated after-sales infrastructure, battery swap logistics, and the sheer informality of SEA’s last-mile ecosystem is getting crowded.

Omoway’s BYD connection gives it credibility and potentially a supply chain edge. But credibility does not equal distribution, and supply chain advantages dissolve quickly when a competitor has deeper dealer networks or better financing schemes for first-time buyers.

The funding is a starting gun, not a finishing line. Execution in SEA’s motorcycle market is where most stories end, not begin.

=======

REGIONAL

Grab completes US$425M acquisition of Stash Financial: The acquisition of the Singapore-based wealth management app deepens Grab’s financial services push, giving it a regulated investment platform and expanding its superapp ambitions beyond payments and lending.

Nadiem Makarim sentenced to 10 years in Chromebook case: Indonesia’s former education minister and Gojek co-founder was convicted of corruption linked to a government Chromebook procurement programme, marking one of SEA’s most high-profile founder-turned-official prosecutions.

TikTok reportedly cut 450 Tokopedia tech jobs: ByteDance-owned TikTok has laid off nearly 450 technology staff at Tokopedia, signalling a sharp consolidation of the Indonesian e-commerce operation following its merger with TikTok Shop.

Alibaba payment firm pays US$600M to resolve US probe: Ant International, Alibaba’s payments affiliate with significant SEA operations, agreed to pay US$600M to settle a US regulatory investigation, in one of the largest fintech enforcement actions involving an Asian firm.

German-listed DDB acquires Infinium Robotics for US$24M: Singapore drone logistics firm Infinium Robotics was acquired by Frankfurt-listed DDB in an all-share deal, marking a rare cross-border public market exit for a SEA deep-tech startup.

Etaily lands Vynn Capital investment for regional push: E-commerce enabler Etaily secured fresh funding from Vynn Capital to accelerate its expansion across Malaysia, Singapore, and Indonesia, targeting brand commercialisation across SEA’s fragmented retail landscape.

BYD co-founder-backed Omoway raises to enter SEA EV race: Electric motorcycle startup Omoway, backed by a BYD co-founder, secured funding to enter SEA’s competitive two-wheeler EV market, where infrastructure gaps and entrenched incumbents remain formidable barriers.

Qashier raises US$6M as SEA SME payments market heats up: Singapore-based point-of-sale startup Qashier, which is profitable, raised US$6M to expand its payments and business management platform across SEA’s underserved SME segment.

Thailand’s Amity Robotics raises US$7M for global push: The Bangkok-based physical AI startup secured US$7M to scale its autonomous service robots beyond malls into global commercial environments, betting on embodied AI as the next hardware frontier.

LinqAlpha raises US$22M to bring agentic AI to investors: Singapore-based LinqAlpha secured US$22M to deploy AI agents that assist public market investors with research and decision-making, targeting institutional and professional investor workflows.

Acti raises US$5.3M to build AI agent layer on keyboards: Singapore startup Acti secured US$5.3M to embed AI agents directly into keyboard-level interactions, positioning the input device as an ambient intelligence layer across enterprise workflows.

AnyMind opens 20 live commerce studios in Indonesia: Tokyo-listed AnyMind is expanding its live commerce infrastructure in Indonesia with 20 new studios, doubling down on Southeast Asia’s fastest-growing social selling market.

SG robotics accelerator cohort raises US$125M, logs exit: Singapore’s robotics accelerator programme produced a cohort that collectively raised US$125M and recorded at least one exit, underscoring the city-state’s growing traction as a deep-tech startup launchpad.

Akro AI raises US$700K to automate regulated data workflows: Singapore-based Akro AI closed a pre-seed round of US$700K to build AI-powered data automation tools targeting compliance-heavy industries such as finance and healthcare.

TransTrack embeds AI across products and operations: Indonesian fleet management platform TransTrack detailed how it is integrating AI across its core product stack and internal operations, reflecting a broader shift among SEA SaaS firms from AI experimentation to deployment.

73% of SEA employees say leadership is digitally disconnected: A Lark survey found nearly three-quarters of Southeast Asian employees believe their leaders lack understanding of real digital transformation needs, exposing a widening gap between C-suite strategy and ground-level execution.

---

INTERVIEWS & FEATURES

Vietnam’s healthtech boom has a talent problem: Rapid growth in Vietnam’s health technology sector is undermined by a chronic shortage of professionals who combine clinical knowledge with technical skills — a constraint that few investors or founders are openly addressing.

VCs writing off Indonesia risk a US$300B mistake: A sharp opinion piece argues that investor pessimism about Indonesia’s startup market is structurally misguided, pointing to domestic consumption, demographic tailwinds, and underserved digital infrastructure as underpriced opportunities.

SEA enterprise fitness tech still has homework to do: As Southeast Asia’s corporate wellness market grows, enterprise fitness platforms struggle with fragmented employer demand, inconsistent engagement, and a benefits culture that has not yet normalised digital health tools.

Qualcomm selects 15 APAC startups for AI innovators programme: Qualcomm named 15 Asia-Pacific startups for its 2026 AI Program for Innovators, offering hardware access, technical mentorship, and go-to-market support to early-stage AI and edge computing companies.

---

INTERNATIONAL

Microsoft launches AI deployment company with US$2.5B commitment: Microsoft has established a dedicated AI deployment unit backed by US$2.5B, a move that positions the company as a direct competitor to systems integrators and managed service providers in enterprise AI rollout.

SoftBank launches AI cloud unit targeting 10-gigawatt capacity: SoftBank’s new AI cloud division plans to tap 10 gigawatts of compute capacity, signalling a massive infrastructure bet that will shape AI availability and pricing across Asian markets including SEA.

Anthropic launches Claude Sonnet 5 for cheaper agent runs: Anthropic’s Claude Sonnet 5 lowers the cost of running AI agents, a development with direct implications for SEA startups building agentic applications on third-party model infrastructure.

Anthropic eyes Samsung for AI chip production: Anthropic is in discussions with Samsung to produce custom AI chips, a potential shift away from TSMC that could reshape semiconductor supply chains across Northeast and Southeast Asia.

OpenAI proposes US government take 5% stake: OpenAI has proposed that the US government acquire a 5% equity stake in the company as it restructures into a for-profit entity, a move designed to neutralise political opposition while deepening state alignment.

Alibaba merges enterprise AI tools to battle Tencent: Alibaba is consolidating its enterprise AI product suite into a unified platform to sharpen competition with Tencent, intensifying China’s cloud and AI wars with implications for SEA enterprise software buyers.

Zuckerberg tells staff AI agents are behind schedule: In an internal address, Meta’s CEO acknowledged that AI agent development has not advanced as quickly as anticipated, a rare admission that tempers expectations around autonomous AI systems across the industry.

Indian tycoon bets US$30M on AI alternative to MS Office: An Indian tech billionaire has committed US$30M to build an AI-native productivity suite designed to compete with Microsoft Office, targeting the vast price-sensitive enterprise market across South and Southeast Asia.

Oriental Semiconductor plans US$212M fundraise: Chinese chipmaker Oriental Semiconductor is planning to raise US$212M, reflecting sustained capital appetite in Asia’s semiconductor sector amid ongoing supply chain restructuring and US export restrictions.

Meta quietly launches vibe-coded gaming app Pocket: Meta’s Pocket app, built using AI-assisted “vibe coding”, marks the company’s latest attempt to capture younger mobile audiences, with potential implications for SEA’s large gaming and social media user base.

UK investors sue Binance and CZ for US$200M: A group of UK-based investors has filed suitagainst Binance and its former CEO Changpeng Zhao over losses from high-risk crypto derivatives products, in one of the largest investor actions against the exchange.

---

CYBERSECURITY

Anthropic’s Fable model frustrates security researchers: Guardrails on the public cybersecurity model are too blunt, researchers say — blocking routine tasks like reading blog posts or writing secure code by triggering keyword-based filters rather than assessing actual risk or intent.

---

SEMICONDUCTOR

IQM, Europe’s first public quantum firm, flags uncertain future: Finnish quantum computing company IQM, the continent’s first publicly listed quantum firm, has acknowledged deep uncertainty about the technology’s commercial timeline, a sobering signal for investors backing quantum plays across Asia.

---

AI

The next AI winners in SEA won’t be AI companies: A sharp analysis argues that the real beneficiaries of AI adoption in Southeast Asia will be sector-specific operators — logistics, healthcare, fintech — that embed AI into existing workflows rather than pure-play AI firms.

The AI layoff trap points straight at Southeast Asia: SEA’s export-driven manufacturing and services economies face disproportionate exposure to AI-driven job displacement, with mid-skill white-collar roles in BPO, shared services, and data processing most immediately at risk.

The accordion effect: how AI expands and compresses work: A framework piece argues that AI alternately expands and compresses the scope of human work in cycles, with implications for how organisations plan headcount, upskilling, and productivity measurement.

The rise of AI twins: from assistant to infrastructure: AI digital twins are evolving from simple productivity tools into embedded organisational infrastructure, shifting how enterprises think about knowledge management, decision support, and workforce continuity.

The death of the traditional org chart via AI: AI is fundamentally restructuring how organisations are designed, making hierarchical org charts obsolete and pushing companies toward fluid, task-oriented team structures built around AI-human collaboration.

Singapore AI and the rise of emotional outsourcing: As Singapore accelerates AI adoption, a commentary examines the growing tendency to delegate emotional and relational tasks — mentorship, feedback, even empathy — to AI systems, and what that costs organisations long-term.

AI slop is a strategy problem, not a content problem: Low-quality AI-generated content, so-called “AI slop“, is proliferating not because tools are inadequate but because organisations lack clear content strategy, editorial standards, and accountability structures.

Why hiring AI experts is the most common startup mistake: Most startups hiring AI specialists in 2026 are solving the wrong problem — the bottleneck is rarely talent, more often unclear use cases, poor data infrastructure, and leadership that has not defined what AI should actually do.

---

THOUGHT LEADERSHIP

Why compute futures make sense in a deflationary market: As AI compute costs fall, a contrarian case argues that futures contracts on compute capacity remain a rational hedge, because demand volatility, not price direction, is the real risk for AI-dependent businesses.

The extreme fear metric and forced liquidation bounces: A market analysis argues that the current crypto market recovery is driven not by renewed conviction but by the mechanical unwinding of forced liquidations — a distinction that matters for anyone reading the bounce as a sentiment shift.

Fringe benefits: disruption starts with unwanted customers: Drawing on Clayton Christensen’s disruptive innovation theory, this piece argues that the most durable startups begin by serving customers incumbents actively ignore and that founders who chase validation from day one misread where markets break open.

What C-dramas teach founders about market entry: Using the global spread of Chinese television dramas as a case study, this commentary draws lessons for SEA founders on cultural localisation, platform leverage, and the sequencing of international expansion.

In Singapore, founders win on foresight, not nerve: A perspective piece challenges the mythos of the bold, risk-taking founder, arguing that Singapore’s most successful entrepreneurs succeed through structured thinking, regulatory navigation, and long-horizon planning rather than bravado.

How centralised crypto exchanges adopted Wall Street’s worst habits: A critical essay argues that CEXs have abandoned crypto’s founding ethos by replicating opaque fee structures, preferential access tiers, and rent-seeking behaviours — and that this will ultimately drive users toward decentralised alternatives.

Everyone told me to write for humans, but they can’t find my page: A practitioner’s essay on the tension between SEO-driven content and human-first writing, arguing that discoverability and readability are not opposites but that most content teams treat them as if they are.

The post Ecosystem Roundup: Why Omoway’s real test hasn’t started yet appeared first on e27.

Southeast Asia’s technology ecosystem has staged a dramatic comeback in the first half of 2026, with total venture funding hitting US$7.4 billion, a staggering 130 per cent jump compared to the US$3.2 billion raised in H1 2025, and a 122 per cent rise over the US$3.3 billion recorded in H2 2025.

The figures, drawn from Tracxn’s Geo Semi Annual Report for SEA Tech H1 2026, paint a picture of a region that has shaken off investor caution and is now pulling serious capital at a pace not seen in years.

Also Read: Southeast Asia’s investors are sleeping on a US$2B ecosystem next door

The resurgence is being driven by a handful of mega-rounds, a red-hot enterprise infrastructure segment, and Singapore’s increasingly iron grip on the region’s funding flows.

The mega-round effect

If there is one story that defines H1 2026, it is the outsized influence of blockbuster funding rounds. The period saw 12 funding rounds of US$100 million or more, triple the four such rounds recorded in H2 2025 and double the six seen in H1 2025.

Leading the charge is DayOne, a Singapore-based data centre company founded in 2022, which closed two tranches of a Series C round totalling a jaw-dropping US$4.5 billion, comprising a US$2.5 billion close in June and a US$2 billion close in January. Backers include Hillhouse, Coatue, and the Indonesia Investment Authority. DayOne’s cumulative funding now stands at US$6.4 billion, making it arguably the most heavily capitalised startup in the region.

Behind it, open-source database platform Supabase raised US$500 million in a Series F round backed by GIC, Accel, and Craft Ventures, while cross-border payments giant Airwallex secured US$320 million in a Series H round, pushing its valuation to US$11 billion, with T. Rowe Price, Baillie Gifford, and QED Investors among those writing cheques.

Other notable rounds include AI video generation platform PixVerse (US$300 million, Series C), AI infrastructure firm SiliconFlow (US$294 million, Series B), and semiconductor packaging company Silicon Box (US$150 million, Series C).

Enterprise infrastructure is the undisputed king

The sectoral breakdown reveals an unmistakable shift towards deep-tech and infrastructure plays. According to Tracxn, Enterprise Infrastructure was the top-performing sector in H1 2026, pulling in US$5.2 billion, a 260 per cent increase from the US$1.5 billion raised in H2 2025, and a remarkable 503 per cent surge compared to the US$870 million raised in H1 2025. DayOne’s twin raises account for the bulk of this, but the appetite for data centre and AI infrastructure assets is clearly structural, not incidental.

Enterprise Applications came in second with US$2 billion raised, up 126 per cent from US$888 million in H2 2025 and 202 per cent from US$666 million in H1 2025. Fintech, by contrast, slipped slightly to US$685 million, down 3 per cent from US$706 million in H2 2025 and 4 per cent from US$711 million in H1 2025, a sign that the segment, long the darling of SEA investors, may be entering a phase of consolidation.

Stage-wise: Late-stage funding explodes

The stage-wise breakdown tells a similarly dramatic story. Late-stage funding skyrocketed to US$6 billion in H1 2026, up 200 per cent from US$2 billion in H2 2025 and 137 per cent from US$2.5 billion in H1 2025. Seed funding also held firm, rising 68 per cent half-on-half to US$328 million, while early-stage investment dipped slightly to US$1 billion, down 8 per cent from US$1.1 billion in H2 2025.

Also Read: How SEA startups can build for real scale

The number of funding rounds, however, continued to decline, from 153 in H1 2025 to 147 in H2 2025, and now 127 in H1 2026. This divergence between fewer deals and far higher capital deployed underscores a broader global trend: investors are concentrating firepower into fewer, higher-conviction bets.

Singapore’s stranglehold on regional capital

The geographic concentration of funding is striking. Singapore-based tech firms captured 94 per cent of all funding in H1 2026, pulling in US$6.9 billion. Bangkok was a distant second at US$116 million (2 per cent), followed by Kuala Lumpur at US$104 million (1 per cent), Taguig at US$60 million (1 per cent), and Jakarta at US$38.2 million (1 per cent).

Singapore’s dominance is consistent with its all-time record of US$63.8 billion in cumulative tech funding, dwarfing Jakarta’s second-place $19.0 billion. For founders and investors across the rest of SEA, the concentration raises real questions about whether regional capital is becoming more geographically lopsided over time.

Exits: Quality over quantity

On the exit front, the story is nuanced. SEA Tech recorded 6 IPOs in H1 2026, down from 9 in H2 2025, but the quality improved markedly. AI company MiniMax debuted at a US$6.5 billion market capitalisation, while SkyeChip and JustCo also went public. The average IPO market cap surged to US$1.1 billion, up from US$270 million in H1 2025, a fourfold improvement.

Acquisitions totalled 19, down 44 per cent from 34 in H1 2025. The standout deal was the acquisition of ST Telemedia Global Data Centres by KKR and Singtel for US$5.2 billion, the largest acquisition in SEA tech in H1 2026, followed by Interplex, acquired by BizLink for $900 million.

Investors: Who’s writing the cheques

On the investor front, EDBI, Asia Partners, and Lion X Ventures led late-stage activity, whilst Vertex Ventures, SEEDS Capital, and Gobi Partners dominated early-stage deployment. At the seed level, Iterative and Antler each made four investments, with 500 Global placing three bets.

Among PE investors, Hillhouse topped the table with two investments, backing DayOne and Airwallex, while Coatue also backed both DayOne and Supabase.

One new unicorn, but the pipeline looks healthy

H1 2026 produced just one new unicorn; cashback platform ShopBack, which hit the milestone in February 2026 after 8.2 years from its Series A and US$350 million in prior funding across nine rounds. That compares to three new unicorns in H1 2025.

Also Read: Singapore’s dConstruct lands US$125M Series A to scale robotics for GPS-denied environments

However, the Soonicorn pipeline (companies likely to cross the US$1 billion valuation mark in the near term) added three new entrants: Startale (Tracxn Score: 63.7), EPG (49.4), and Edena Capital (29.7), all Singapore-based.

The bigger picture

Benchmarked globally, SEA accounted for 2 per cent of worldwide tech funding in H1 2026, ranking fourth by country behind the US (US$366 billion), China (US$21.1 billion), and the UK (US$14.5 billion), but on par with India (also at US$7.4 billion). With the region’s CAGR for funding over the last two years running at 56 per cent, the trajectory is clear: Southeast Asia is back in the game, and investors are paying attention.

—-

Data sourced from the Tracxn Geo Semi Annual Report, SEA Tech H1 2026. All figures are equity funding only and exclude debt, grants, post-IPO, and ICO funding.

The post Enterprise infra just became SEA’s most explosive sector, surging 503 per cent YoY appeared first on e27.