Over the past few years, the conversation around startup funding has started to shift. Not in a dramatic way, but in how capital is actually distributed across stages.

For a long time, venture capital sat at the centre of everything. Founders built it with it in mind from day one. The path felt clear. Raise early, scale fast, move through rounds. That model still exists, and venture capital continues to play a major role globally. Strong companies are still being funded, large rounds are still happening, and the system itself remains active.

But the shape of that system has changed.

Globally, venture capital today tends to engage more decisively when there is clearer traction. Businesses that show stronger execution, more defined markets, and visible growth pathways are where attention concentrates more naturally. This does not reduce the presence of venture capital. It shifts how and when it becomes most relevant.

In Southeast Asia, this pattern is even more visible. The ecosystem has matured, and with that comes a clearer separation between stages. Venture capital is still very much part of the journey, but it tends to play a more central role as companies move into growth phases, where there is already a base to build on.

At the earlier stages, the picture looks different.

Early-stage capital is still active, but it no longer sits in one place. It is distributed across a wider set of channels. Accelerators, incubators, venture studios, corporate programs, and a growing layer of grant and hybrid funding systems all play a role in supporting new ventures. In many cases, this is where early momentum is built.

These channels are not simply smaller versions of venture capital. They operate with different objectives. Some are designed to support ecosystem development, others focus on specific sectors, and many are aligned with institutional or policy-driven goals. The capital is there, but it sits within frameworks that are structured differently.

From the outside, this creates a sense that there is more capital available across more sectors than before. In many ways, that is true. But it also means that accessing this capital requires a clearer understanding of how each part of the system works.

A founder today may engage with multiple funding pathways at the same time. Applying to an accelerator, exploring grant opportunities, speaking to early-stage investors, and preparing for future venture rounds. The intent is to increase the chances of funding. The effort is broad.

What often remains unchanged is the approach.

Also Read: Why inclusive hiring matters for a startup ecosystem

The same narrative, the same materials, and the same assumptions are carried across different funding channels. A pitch designed for venture capital is used in a grant application. A grant proposal is framed with a venture-style story. Adjustments are made, but the core remains largely the same.

When outcomes do not change, it becomes difficult to interpret. It begins to feel like a reflection of the venture itself.

In reality, each funding channel operates as a filter before it becomes an opportunity. The evaluation begins with alignment. Stage, geography, structure, use of funds, and intent all shape how an application is read. These are not always visible from the outside, but they influence outcomes early in the process.

If the alignment is off, the process rarely moves forward in a meaningful way.

Because these filters are not always clearly communicated, founders tend to focus on what they can control. The pitch is refined, the message is sharpened, the projections are updated. Then the process repeats, often across multiple programs.

Over time, this creates a pattern where effort increases, but outcomes remain similar.

Seen from a broader perspective, this is less about the absence of capital and more about how that capital is structured.

Venture capital continues to play a central role, particularly as companies move into stages where scale and growth become more visible. At the same time, early-stage capital is active across programs, institutional channels, and sector-focused funding systems. Each layer is functioning, but each operates differently.

The result is a funding landscape that is more layered than before.

Also Read: Unlock your enterprise agility to unleash the potential of your startup

For founders, this means that the journey is no longer about approaching a single source of capital. It is about understanding how different forms of capital connect to different stages of the business. Moving through these layers in a way that matches the venture’s current position becomes increasingly important.

Without that alignment, the process can feel repetitive. Applications go out, responses come back, and the cycle continues without clear movement. With it, the same landscape begins to make more sense.

The capital is there. In many ways, there is more of it across more channels than before.

The difference lies in how it is approached and how well each venture fits within the system it is entering.

Understanding where startup money is really coming from today is not just about identifying new sources. It is about recognising that each source operates on its own logic, and aligning with that logic early in the journey.

Because in today’s funding landscape, there is no shortage of capital conversations. Only a shortage of alignment.

And if everything still feels confusing, there is always the classic fallback. 
Update the pitch deck, change the font, add a bigger market size slide, and try again.

Sometimes that works.

Just not for the reasons most people think.

So before doing that, it is worth stepping back and looking across the full spectrum of capital available today, venture capital, grants, blended finance, catalytic capital, and everything in between, and deciding where the real fit actually is.

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The Philippines’s services sector is indisputably the country’s engine of growth and employment. Today, it accounts for more than half of the national GDP and has posted growth rates that, at times, rival those of fast‑growing economies in the region.

Yet beneath headline growth lies a structural challenge: much of the expansion has been concentrated in labour-intensive, low‑productivity activities—traditional retail outlets, basic hospitality, and standardised business process outsourcing (BPO) roles. These activities provide essential jobs and incomes, but they lack the capital intensity, technological sophistication, and organisational complexity necessary for sustained gains in output per worker.

Also Read: The hidden tax on Philippine SMEs: Unreliable infrastructure

From a “Services Growth Problem” to a “Services Model Problem”

As per the Philippine Private Capital Report 2026 by Foxmont Capital Partners, this is less a services growth problem and more a services model problem. Persisting with expansion primarily through increased headcount in low‑productivity roles risks trapping the Philippines at middle‑income levels, aka the middle‑income trap. Breaking through requires reimagining how services are delivered: shifting toward knowledge‑based, system‑driven, and technology‑enabled models that raise productivity per worker and increase the share of value captured domestically.

Why the distinction matters:

• Employment expansion absorbs millions of new labour market entrants and supports livelihoods across regions.
• Productivity growth determines sustainable improvements in living standards and the country’s fiscal capacity to finance public services.

Retail: Platformisation, logistics, and the sari‑sari store opportunity

Traditional retail (wet markets, sari‑sari stores, and neighbourhood shops) remains central to Philippine commerce, particularly in provincial and low‑income urban communities. These formats are low‑capital, labour‑intensive, and typically show low output per worker.

The rise of e‑commerce is beginning to decouple retail growth from physical store expansion. Platform ecosystems such as Shopee and Lazada have catalysed productivity gains through data‑driven merchandising, dynamic pricing, and logistics optimisation. While platform giants report very high productivity per worker in other markets, the Philippines can capture similar qualitative benefits by adapting models to local contexts.

Policy and business levers for retail transformation:

• Expand affordable broadband and mobile connectivity to shrink the digital divide between urban and rural areas.
• Improve last‑mile logistics: invest in local sorting hubs, cold chain for perishables, and rural delivery partnerships.
• Upskill micro‑merchants: provide simple digital tools and training so sari‑sari owners can adopt digital payments, inventory apps, and online listings.
• Strengthen financial services: micro‑credit and invoice financing enable merchants to stock higher‑value inventory and smooth cash flow.
• Ensure platform governance: enforce consumer protection, fair competition, and data privacy while encouraging local innovation.

IT‑BPM: Moving up the value chain with AI and analytics

The Philippines has built a world‑class IT‑BPM industry centred on voice‑based contact centres and transactional back‑office work. The sector has been a major export earner and employer in Metro Manila, Cebu, Clark, and other hubs. Yet automation threatens routine tasks, and scaling by sheer headcount offers diminishing returns.

Also Read: Philippines’s productivity problem starts in the classroom

The next wave of growth lies in higher‑value segments: analytics, knowledge process outsourcing (KPO), engineering services, digital transformation consulting, and AI‑enabled operations. These segments raise output per worker by embedding automation, natural language processing, and predictive analytics into workflows—reducing handling time, increasing first‑contact resolution, and offering advisory services that command higher fees.

Actions to accelerate IT‑BPM upgrading:

• Reform education: strengthen university and technical curricula in data science, software engineering, and domain specialisations (healthcare, fintech, legal process outsourcing).
• Boost lifelong learning: subsidise bootcamps, certifications, and company‑sponsored reskilling programs for mid‑career workers.
• Incentivise high‑value investment: targeted tax and non‑tax incentives for companies establishing analytics, R&D, and innovation centres in the Philippines.
• Promote local scaleups: expand venture capital and growth financing for SaaS firms and analytics platforms that can export services regionally.

Policy and institutional foundations

Transformation requires coordinated public‑private action across infrastructure, education, finance, regulation, and regional development.

Key levers include:

• Digital infrastructure: accelerate nationwide fibre builds, tower deployment, and spectrum allocation to make high‑quality connectivity ubiquitous.
• Education and training: integrate computational thinking and digital literacy into K–12, expand technical-vocational education, and scale industry‑aligned short courses.
• Financial inclusion: deepen digital payments, merchant lending, and credit scoring using alternative data to unlock SME investments in productivity tools.
• Regulation and trust: strengthen data protection laws and regulatory clarity for cloud services and cross‑border data flows to attract enterprise customers.
• Regional diversification: create incentives and shared infrastructure so higher‑value service hubs develop in Cebu, Davao, Iloilo, Clark, and other cities—reducing congestion in Metro Manila and tapping local talent pools.

Social considerations and inclusive transition

Technology and productivity gains can displace certain roles even as they create higher‑value ones. Managing this transition requires active labour market policies:

Also Read: Philippines’s quiet AI revolution is about work, not tech

• Reskilling and upskilling programs targeted at workers most likely to be affected by automation.
• Transition support and portable social protections (e.g., unemployment insurance, wage subsidies during retraining).
• Community outreach and accessible training channels—mobile training units, local government partnerships, and employer commitments to hire trained workers.
• Inclusive growth also means bringing women and underrepresented groups into higher‑value services through scholarships, targeted training, and workplace flexibility measures.

Realistic timelines and the Philippines’s comparative advantages

The shift from labour‑intensive to knowledge‑ and tech‑driven services is multi‑year but achievable within a decade with concerted effort. The Philippines has several strengths to build on:

1. A large, English‑proficient, young workforce.
2. An established BPO ecosystem and global reputation for service delivery.
3. Robust remittance inflows support domestic demand and entrepreneurship.
4. Growing digital consumer adoption and a vibrant startup scene.
5. Targeted policy, industry leadership, and investments in human capital can move the country from employment‑centric growth to productivity‑centric expansion.

Conclusion: Services that create value at scale

The Philippines’s services sector can remain a major employer while becoming a source of higher productivity and sustainable economic growth. The pathway out of the middle‑income trap is not merely more jobs, but better, higher‑value jobs that scale through knowledge, systems, and technology rather than headcount alone.

Also Read: From assembly line to innovation engine: Can Philippines climb the chip value chain?

When retail becomes platform‑enabled and IT‑BPM evolves into analytics and AI‑led services, output per worker rises, wages grow, and the economy creates more value with less proportional labour input—an outcome that benefits workers, firms, and the nation.

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Thailand’s cybersecurity ecosystem expanded steadily through 2025, propelled by rapid digital transformation, surging cloud adoption, and stepped‑up investment in national data infrastructure.

Progress, however, has not been matched by commensurate operational depth. Ransomware campaigns have become more surgical, targeted attacks on financial institutions have intensified, and advanced persistent threats are showing up with greater frequency.

Also Read: Cyber risk is moving upstream but we’re still defending downstream

The result is a market that has built institutional scaffolding but still struggles to convert policy and procurement into resilient, repeatable defence.

A tense balance: Institutional gains vs operational shortfalls

Regulatory tightening and government digital initiatives have improved the country’s cybersecurity posture on paper. Data‑protection frameworks and cross‑agency programmes encourage firms to formalise security strategies. Yet intentions rarely substitute for capability.

Many organisations lack the in‑house talent and mature processes needed for effective detection, containment, and recovery. Demand for cybersecurity outpaces the available local expertise, prompting firms to rely heavily on managed security providers or fragmented point solutions.

This is not just a resource problem; it is a structural one. Budgeted security projects often deliver tactical improvements, but long‑term execution — continuous monitoring, threat hunting, architecture rework, and secure cloud migration — remains uneven. Smaller enterprises are particularly exposed: limited budgets and low security maturity make them easy targets and weak links in supply chains.

Threat landscape: Smarter adversaries, wider targets

The past year saw attackers shift from opportunistic intrusions to targeted, multi‑stage campaigns. Ransomware groups increasingly deploy double extortion tactics — encrypting data and threatening public leaks — amplifying reputational and regulatory risk. Financial institutions, with complex third‑party ecosystems and cloud dependencies, have been high‑value targets for bespoke campaigns that exploit misconfigurations and weak identity controls.

Meanwhile, the expansion of IoT and the rollout of 5G technologies are widening the attack surface. Smart factories, logistics systems, and digital healthcare services introduce operational technology (OT) into the threat matrix, where legacy devices and long upgrade cycles make patching and segmentation difficult. The country’s growth in cloud services and data centres increases both exposure and potential blast radii for incidents.

Technology response: AI, cloud‑native security and platform thinking

Defenders are moving beyond single‑tool approaches. The market is trending towards AI‑driven detection, cloud‑native controls, and identity‑centric security. Organisations are investing in platforms that ingest telemetry from the cloud, endpoints, and identity systems and apply analytics to detect anomalies that humans would otherwise miss.

Also Read: When security fails, trust breaks: Why cybersecurity is now a business priority

Managed security service providers (MSSPs) and outcome‑based models have surged in popularity as firms seek to close the talent gap quickly. MSSPs offer 24/7 monitoring and triage, but outsourcing comes with its own risks: opaque performance metrics, dependency on third‑party operational models, and potential systemic exposure if a provider is compromised. Boards must avoid treating MSSP engagement as a checkbox; robust contract governance and independent validation are essential.

The role of local vendors and integrators

Thailand’s market is a hybrid of global vendor platforms and local implementers. Multinational firms bring research, scale and broad telemetry, but localisation, regulatory alignment and on‑the‑ground integration are frequently delivered by domestic system integrators and service firms. Local companies such as BullVPN, UpperVPN, and NotVPN are carving niches in VPN and enterprise security services, providing context‑aware solutions tailored to Thai enterprises.

This division of labour is pragmatic: global technology solves for capability gaps; local players ensure technology fits the market and regulatory context. Yet it also underscores a skills arbitrage — advanced threat research and high‑end engineering often remain concentrated in overseas teams, leaving Thai organisations dependent on imported expertise for complex incident response.

Talent: The invisible bottleneck

The recurring refrain across industry and government is simple: talent shortage. There is a dearth of cloud security engineers, threat hunters, red‑teamers, and forensic investigators. Educational institutions produce graduates, but not at the scale or specialisation required to staff continuous security operations. This gap manifests as longer detection times, inconsistent patching regimes, and heavy reliance on MSSPs.

Fixing this requires more than curriculum tweaks. Apprenticeships, industry placements, public‑private training initiatives, and retention incentives are needed to create career paths in cybersecurity. Without a pipeline that rewards advanced specialisation and keeps talent local, Thailand will continue to import critical skills at a cost to resilience.

Regulation, collaboration and the limits of compliance

Regulatory enforcement is intensifying, with compliance becoming a baseline requirement for participation in certain sectors. Public‑private cooperation has evolved from advisory forums into more operational partnerships, but information sharing remains inconsistent. For compliance to translate into real security, it must be bound to operational maturity: incident simulations, shared threat intelligence, and sectoral playbooks.

Compliance alone will not deter sophisticated attackers. As Tracxn summed it up succinctly: “Weaknesses persist in talent and SME readiness.” Regulatory frameworks can raise the floor, but the ceiling is determined by investments in people, process and cross‑domain platforms.

Industrial risk: OT, 5G and the cost of fragmentation

Industrial digitalisation highlights the cost of fragmented security stacks. Securing hybrid environments that span cloud apps, mobile endpoints and industrial controllers requires unified visibility and consistent policy enforcement. Legacy OT devices with limited security capabilities complicate segmentation and incident response. The need for an integrated platform security — rather than a patchwork of point tools — is urgent for organisations that rely on connected operations.

What happens next

Thailand’s cybersecurity market is maturing but remains fragile. The coming years will be pivotal: success depends on translating regulatory momentum into operational muscle. That means building local talent, standardising cross‑sector information sharing, investing in cloud‑native defences powered by AI and resisting the temptation to treat MSSPs as a panacea.

Also Read: Asia’s new cyber threat: AI that speaks your language

A sober reality check: progress is meaningful but reversible. Without sustained investment in people, processes and unified platforms, rapid digital adoption risks amplifying systemic vulnerability rather than resilience. The country must do more than adopt world‑class technology; it must embed world‑class execution.

“Weaknesses persist in talent and SME readiness,” Tracxn observes, crystallising the central dilemma. Thailand can design frameworks and buy technology, but execution — the grunt work of detection, iteration and accountability — will determine whether growth becomes a liability or a source of durable strength.

The post Thailand’s cybersecurity boom has a weak core appeared first on e27.

Groundup AI, the Singapore-born startup that pioneered what it describes as the world’s first Agentic AI for Cognitive Maintenance, has closed its most significant commercial agreement to date. The contract, valued at more than US$10 million, involves the large-scale deployment of the company’s flagship platform across multi-site critical operations for one of the world’s leading asset-heavy organisations.

The identity of the client has not been disclosed for strategic reasons, but the scope of the agreement signals growing institutional confidence in AI-driven approaches to industrial uptime and asset management. The deal was secured following a rigorous competitive evaluation process.

Traditional predictive maintenance has long been the industry standard, offering organisations advanced warning of potential equipment failure. Groundup AI’s proposition moves a step further, positioning its platform as a system capable of autonomous diagnosis, reasoning, and operational guidance. The startup terms it Cognitive Maintenance.

The platform is underpinned by the Groundup AI Asset Library, a proprietary repository of more than 5,000 anomaly signatures that enables deep-tier pattern matching and root cause analysis. The company’s in-house AI, GINA AI, continuously learns from live operational environments, improving diagnostic accuracy over time without requiring lengthy onboarding or complex deployment cycles.

“The world is ready for Cognitive Maintenance that reasons, diagnoses, and guides,” according to Leon Lim, CEO and Founder, Groundup AI, in a press statement.

Also Read: Human imposter syndrome magnified: When AI knows more than we ever could

Regional ambition

The record-breaking contract follows a period of rapid growth for Groundup AI. In April 2025, the company closed a US$4.25 million Series A funding round led by Tin Men Capital, with participation from Wavemaker Partners, SEEDS Capital, and HIVEN, the venture capital arm of CJ International Asia. That capital injection has allowed the company to accelerate its technical development and expand its footprint across the region.

Alex Wong, COO and Co-Founder of Groundup AI, described the contract as an opportunity to execute the company’s core mission at unprecedented scale. The firm has trained its sights on the manufacturing, maritime, and critical infrastructure sectors, which it believes hold billions in untapped value that smarter maintenance systems could unlock.

Groundup AI’s technology combines industrial Internet of Things infrastructure with proprietary acoustic sensors and autonomous AI agents to deliver what it calls Physical AI reliability. The platform is designed to bridge the gap between the domain expertise of experienced engineers and the pattern-recognition capabilities of machine intelligence.

Yong Tai, Sales Engineer and Founding Team Member, highlighted that the company’s ambitions extend beyond software. “From heavy industries to advanced manufacturing, this milestone is about finally bridging the gap between raw data and action on the ground,” he said.

As global industries face mounting pressure to reduce unplanned downtime, cut operational costs, and meet sustainability targets, the case for Cognitive Maintenance is becoming increasingly difficult to ignore. Groundup AI’s latest contract suggests that at least one major organisation is prepared to make that transition at scale — and that the era of autonomous industrial reliability may already be underway.

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Image Credit: Groundup AI

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Deeptech startups face a unique management challenge: how do you build a business around a technology that can take years to develop, when market trends and customer needs may shift every few months?

Big companies like Nvidia, Google, or Meta can take on these kinds of problems with large, highly specialised engineering teams and deep reserves of capital. Even then, success isn’t guaranteed. Startups don’t have that luxury. They operate with limited resources, and any sudden change in the market can render years of painstaking work irrelevant.

Conventional startup wisdom suggests analysing the market, identifying a niche, and then building a product to fit it. But in deeptech, this model often breaks down. By the time the technology is ready, the “perfect niche” may have disappeared or evolved into something entirely different.

Why invest in an engineering challenge over a market niche

When GPU Audio launched in 2017, the team made a deliberate choice: rather than chasing an existing niche, they decided to solve a fundamental engineering problem that could survive multiple shifts in the market. Their focus was on adapting graphics processing units (GPUs), which were originally designed for parallel rendering of graphics, to process audio data in real time.

It wasn’t a quick win. Between 2012 and 2015, the team built multiple prototypes and failed more often than they succeeded. A turning point came in 2017, when a leading researcher — a former advisor to Qualcomm’s founders and a recognised authority in ray-tracing engine design — joined the project.

Even then, it took years before the first working demonstrations appeared in 2020 and 2021, nearly a decade after early experiments began. During that time, GPU architectures changed, AI went mainstream, and consumer trends shifted dramatically. But the team stayed focused on their core technical goal, resisting the temptation to pivot toward short-lived opportunities.

In this case, prioritising long-term technological resilience helped the team navigate several market cycles and uncover opportunities that weren’t on the radar at the start. For teams in similar situations, it shows that this path — while risky and demanding a long planning horizon — can sometimes open unexpected doors.

The depth and complexity of deeptech engineering challenges

The obstacles GPU Audio faced illustrate why deeptech startups must often think differently. Two problems in particular stood out.

First, GPUs were never designed for audio. For graphics, small delays are acceptable — a dropped frame may go unnoticed. But audio requires near-instant precision. Even a tiny delay of just a few milliseconds can be audible, disrupting the experience entirely.

Second, the way sound is processed is fundamentally different from graphics. GPUs excel at handling millions of identical, independent tasks in parallel — the computational equivalent of a factory full of workers all performing the same action simultaneously. Audio, on the other hand, is a chain of small, interdependent steps. Each calculation depends on the result of the one before it. Getting GPUs to handle this kind of workload required more than optimisation — it demanded a reinvention of how audio processing itself could be structured.

Also Read: From pilot to scale: Why traditional VC metrics don’t work for climate deeptech

Challenges of this scale affect more than just the engineering roadmap — they shape how a team operates. Long development cycles call for clear, ongoing communication with both investors and internal teams. In our case, we made a point of sharing intermediate milestones to keep everyone aligned, even when the road to a finished product stretched over years.

Creativity in engineering: Borrowing ideas from adjacent fields

Without the deep pockets of a tech giant, GPU Audio needed more than persistence; they needed creativity. The team had to rethink audio algorithms from scratch, searching for ways to break down sequential tasks into parallelisable ones.

The breakthrough came by borrowing ideas from another domain: ray tracing in 3D graphics. The company’s chief scientist had extensive experience in this field, having built one of the fastest ray-tracing engines in the world. Ray tracing calculates reflections, shadows, and interactions across countless objects at once — problems not unlike the hundreds of processes required in real-time audio.

Applying these principles, the team built a new kind of audio process manager — a scheduling system that could aggregate audio streams, distribute workloads efficiently, and maintain the responsiveness required for real-time sound. What the industry had long dismissed as technically impossible suddenly became feasible.

Shifting from consumer products to developer SDKs

Solving the core technical problem was only half the battle. Next came the question of product-market fit. Breakthrough engineering doesn’t automatically translate into customer demand — especially in markets where preferences shift quickly.

Initially, GPU Audio released consumer software for musicians and sound engineers, tools that ran on GPUs rather than CPUs. While useful, this approach wasn’t scalable. The team realised that instead of building end-user products themselves, they could multiply their reach by offering a software development kit (SDK) to other application developers.

Also Read: Funding deeptech: Balancing potential and complexity in the search for capital

This shift made the technology more flexible, less tied to short-term consumer trends, and far more attractive to potential partners. It also created a pathway into industries that the founders hadn’t originally targeted. For some deeptech startups, shifting from direct-to-consumer products to an SDK model can provide more flexibility and make it easier to keep pace with changing industry needs — that was the case here.

Discovering unobvious opportunities

Moving to an SDK unlocked a surprising new vertical: the automotive industry.

Car audio systems were lagging behind broader automotive innovation. Even as electric vehicles, advanced infotainment systems, and autonomous driving became more sophisticated, most in-car audio processing still relied on outdated DSP chips. GPU Audio saw an opportunity to modernise this layer.

The company developed zoned audio technology — allowing different passengers to hear different content simultaneously without interference. A driver could take a call over the front speakers while children in the back seat enjoyed a movie, all without overlapping sound.

This innovation didn’t just improve in-car entertainment; it opened the door to entirely new use cases, from personalised multimedia systems to interactive voice-based services. It also showed how a deeptech startup could scale by partnering with established industries, repurposing its core technology to meet needs far beyond the original vision.

The takeaway here is to stay open to markets beyond the original target segment. Often, meaningful scale comes from industries that haven’t yet gone through a full wave of innovation.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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