For years, cybersecurity teams have told employees to look for the usual clues: awkward grammar, strange email addresses, urgent requests, and links that do not quite look right. Generative AI is making that advice less reliable.

A new Mimecast study suggests that many organisations in Asia Pacific now expect attackers to use AI against them, yet a large number still have not adjusted their defences to match the threat.

According to the company’s “State of Human Risk 2026” study, 65 per cent of surveyed IT and security decision-makers believe an AI-enabled attack against their organisation is inevitable within the next 12 months.

Also Read: GoSerpent exposes the quiet cyber war against Southeast Asian governments

The APAC findings are based on responses from 500 IT security and IT decision-makers in Singapore and Australia, drawn from a broader global survey of 2,500 respondents across nine markets. All organisations surveyed had more than 250 employees and more than 250 email users, spanning sectors including financial services, healthcare, technology, manufacturing, retail, energy, public services, construction and media.

The headline number is striking, but the more important finding may be this: 60 per cent of APAC respondents said their organisation was not fully prepared to deal with AI-driven threats that exploit human vulnerabilities. In other words, many companies can see the risk coming, but their playbooks are still catching up.

The new face of social engineering

Social engineering attacks are not new. Fraudsters have long pretended to be bosses, suppliers, banks, government agencies or colleagues to trick employees into revealing credentials, approving payments or sharing sensitive data.

What AI changes is the quality and scale of deception.

Attackers can now use generative AI tools to write polished emails, imitate a company’s tone, translate messages into local languages, personalise scams using scraped public information, and produce convincing voice or video impersonations. For businesses in Southeast Asia, where cross-border teams often work across English, Mandarin, Bahasa Indonesia, Vietnamese, Thai and other languages, this matters. Poor language used to be one of the easiest warning signs of a scam. That signal is becoming weaker.

“AI is changing the way cybercriminals manipulate trust,” said Nicky Choo, Vice President and General Manager, APAC, Mimecast. “Attackers can now use it to create convincing, tailored messages that appear to come from a colleague, a partner or a senior leader.”

Also Read: Southeast Asian SMEs remain soft targets as ransomware groups refine extortion tactics

That is particularly relevant in regional markets where startups, SMEs and large enterprises alike rely heavily on fast-moving digital communication. A procurement request may arrive by email, be clarified on a messaging app, approved through a cloud workflow, and paid through a banking portal. Each handoff creates a moment where an employee has to decide whether the person on the other side is genuine.

Mimecast’s study found that 79 per cent of respondents were concerned about AI being used as an attack vector against their organisation. Two-thirds, or 66 per cent, agreed that an employee in their organisation was very likely to be fooled by a cybercriminal using AI as part of a social engineering attack.

That finding points to a difficult reality for security leaders: the weak point is not simply technology. It is judgement under pressure.

Training has not caught up

The study found that AI-specific employee preparation remains limited. Only 40 per cent of surveyed APAC organisations provide training on how to use AI while avoiding exploitation, while 42 per cent conduct simulated AI-driven phishing attacks.

This does not mean employees are receiving no cybersecurity training at all. Many companies already run phishing awareness programmes, password hygiene sessions or compliance modules. The gap is that traditional training may not prepare workers for scams that sound natural, reference real business context, and arrive through channels they use every day.

“Employees should not be expected to identify increasingly sophisticated deception on instinct alone,” Choo said. “Fewer than half are training staff on how to avoid AI-driven exploitation or running simulated AI phishing exercises.”

For Southeast Asian companies, this gap could widen as AI adoption accelerates inside the workplace. Employees are experimenting with AI assistants for writing, coding, customer support, research and translation. At the same time, attackers are using similar tools to improve fraud. That creates a messy middle ground where legitimate AI use and malicious AI use can look increasingly similar.

Also Read: Thailand is suddenly on the frontline of a new ransomware wave

A finance employee may receive a payment request written in the exact style of a senior executive. A customer support agent may be sent a forged document that looks credible. A founder may hear what sounds like an investor or board member on a voice call. The problem is not that workers are careless. It is that the cost of verifying trust has gone up.

Why APAC firms face a sharper test

APAC’s exposure is not uniform, but several regional factors make the issue more pressing. Singapore and Australia, the two markets covered in the APAC sample, are both highly digitised economies with mature financial and enterprise technology sectors. They are also hubs for regional business activity, meaning employees frequently deal with overseas vendors, remote teams and cross-border customers.

In Southeast Asia, the challenge is compounded by uneven cyber maturity. Large banks, telcos and technology firms may have advanced controls, while smaller companies in their supply chains often operate with lean security teams. Startups can be especially vulnerable because they prize speed, informality and rapid decision-making, the same conditions that social engineers exploit.

A young company may not have layered approval systems for payments or data access. A fast-scaling regional business may onboard new staff and vendors faster than it updates security processes. In such environments, a convincing AI-generated message does not need to defeat sophisticated infrastructure; it only needs to land at the right moment.

The growing use of collaboration tools also expands the attack surface. Email remains central, but work now happens across Slack, Teams, WhatsApp, Telegram, shared documents and customer platforms. If security awareness is still built mainly around spotting suspicious emails, organisations may miss deception that begins elsewhere.

From blocking threats to building judgement

Mimecast argues that organisations need to treat human judgement as a core part of cyber defence, not merely as the last line of protection when technical filters fail. That means pairing security tools with practical training, realistic simulations and clearer verification processes.

For example, companies can require out-of-band confirmation for payment changes, create simple escalation paths for suspicious requests, and train staff on AI-specific red flags such as synthetic voice calls, overly personalised messages or unusual urgency framed in familiar language. Security teams can also run simulations that reflect how employees actually work, rather than relying only on generic phishing tests.

The broader lesson is that AI-enabled cyber risk is not just a technical problem to be solved by buying another tool. It is an organisational problem involving culture, process and incentives. Employees need permission to slow down, question authority and verify unusual requests without fearing that they are blocking business.

Also Read: From fraud fighters to zero-trust builders: SEA’s cyber stars

The Mimecast study is a warning, but not an unexpected one. As AI lowers the cost of producing convincing deception, the old assumption that scams are easy to spot will become increasingly dangerous. For APAC organisations, the next phase of cybersecurity may depend less on whether employees can catch every fake, and more on whether companies design systems that do not leave them to make those calls alone.

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Singapore’s central bank is drawing a firmer line between digital assets built for speculation and those that could make financial markets work better, as global crypto exchange Coinbase expands its footprint in the city-state.

Speaking at the opening of Coinbase’s new Singapore office, Kenneth Gay, Chief Fintech Officer at the Monetary Authority of Singapore (MAS), said the digital asset industry has moved beyond the early public debate around cryptocurrencies and trading. The more important question now, he suggested, is whether tokenisation and digital money can solve practical problems in finance without weakening trust, resilience, market integrity, or consumer protection.

Also Read: Singapore crypto adoption hits new high as 61 per cent now hold digital assets

That distinction matters in Southeast Asia, where cross-border payments remain costly and slow, capital markets are fragmented, and businesses often operate across multiple currencies and regulatory regimes. For years, the region’s crypto boom was associated mainly with retail trading, volatile tokens, and collapses that hurt ordinary users. MAS’s latest framing points to a different phase: one focused less on hype and more on settlement, compliance, liquidity, and institutional infrastructure.

Gay said the ecosystem is becoming “more diverse and more differentiated”, with use cases emerging across tokenised financial assets, payments, custody, compliance, settlement, and market infrastructure. Some tokens, he noted, are designed largely for speculation. Others are tied to real financial assets, commercial bank money, or central bank payment instruments.

The implication is clear: MAS is not treating all digital assets the same way. It wants to support the parts of the market that may improve how finance operates, while maintaining close oversight of activities that create risk without obvious economic value.

From crypto trading to financial plumbing

The most promising developments, according to Gay, are those that address long-standing frictions in the financial system. Tokenisation — the process of representing assets such as bonds, funds, deposits, or collateral on digital ledgers — is often marketed as a breakthrough in itself. MAS’s view is more restrained: the value is not in turning an asset into a token, but in whether doing so improves how that asset is issued, transferred, pledged, settled, or managed.

In practical terms, tokenised assets could reduce the time institutions spend reconciling records, improve post-trade processes, and make settlement more predictable. This is especially relevant in Asia, where capital and trade flows frequently move across time zones, jurisdictions, and currencies. A company operating between Singapore, Indonesia, Vietnam, and the Philippines may face different banking rails, settlement cycles, and compliance requirements in each market.

But MAS is also warning that tokenised assets cannot scale in isolation. Faster-moving assets require settlement money that can move just as safely and reliably. Without credible settlement assets, trusted infrastructure, legal certainty, and operational controls, tokenisation risks becoming another layer of complexity rather than a solution.

That is why digital money has become central to Singapore’s digital asset strategy. Gay pointed to three forms now under active development: well-regulated stablecoins, tokenised deposits, and central bank digital currencies, particularly wholesale CBDCs used between financial institutions.

Each serves a different purpose. Properly backed stablecoins, if governed by strong safeguards, could become settlement assets or mediums of exchange. Tokenised deposits could allow banks to bring commercial bank money into digital environments. Wholesale CBDCs could provide a credit risk-free settlement asset for financial institutions.

The future, in MAS’s view, will not be built around a single ledger or one form of digital money. Instead, multiple digital assets, payment instruments, and ledgers are likely to coexist. The policy challenge is to make sure they are interoperable, safe, and aligned with financial regulation.

What BLOOM is trying to solve

To push that agenda, MAS has established BLOOM, short for Borderless, Liquid, Open, Online, Multi-currency. The initiative is designed to support multi-currency settlement capabilities and bring together banks, payment service providers, architects, and other industry participants to work on practical use cases for digital money and settlement assets.

Also Read: Asia’s US$4T tokenisation boom: Why the region will lead the global financial revolution by 2030

Gay described BLOOM as being about “making digital money useful for real settlement needs across borders and currencies”. That phrasing captures the bigger shift in Singapore’s approach. The goal is not to encourage experiments for their own sake, but to help projects move from pilots to commercially meaningful deployment.

For Southeast Asia, this focus is important. The region has no shortage of fintech pilots, blockchain proofs of concept, and bank-led experiments. What it lacks, in many cases, is the connective tissue that allows regulated institutions to deploy such systems at scale across markets.

Interoperability, liquidity management, and programmable compliance — the ability to embed compliance rules into transactions — are not glamorous topics, but they determine whether digital asset infrastructure can move into production.

MAS said BLOOM is intended to give participants regulatory clarity, partnerships, and ecosystem connectivity to scale and launch in Singapore. The regulator also expects to learn from these projects, using real-world experience to shape future policy and rules.

Coinbase’s involvement in BLOOM places it within this institutional push. Gay thanked the company for its active participation and said firms such as Coinbase are building capabilities, launching product offerings, and creating jobs in Singapore.

Coinbase in a crowded regional race

Coinbase’s Singapore expansion comes as major global exchanges compete for regulatory legitimacy in Asia. The company holds a Major Payment Institution licence from MAS, allowing it to provide digital payment token services in Singapore. Its regional rivals include Crypto.com, Independent Reserve, and Blockchain.com, which have also received MAS licences, while global names such as OKX, Kraken, Gemini, and Binance compete for institutional and retail users across different markets, subject to local rules.

The competitive landscape has changed since the peak of the crypto bull market. Exchanges can no longer rely solely on retail trading volumes or brand recognition. In Singapore, the more valuable prize may be integration into regulated financial infrastructure — custody, settlement, tokenised assets, and stablecoin-based services that appeal to institutions rather than speculative traders.

This is also where MAS’s regulatory posture gives Singapore an edge. The city-state has tightened rules around retail access and consumer protection, while continuing to support institutional experimentation through projects involving tokenisation, wholesale settlement, and digital money. That balance has made Singapore one of the more important hubs for digital asset companies seeking credibility in Asia, even as other centres such as Hong Kong, Dubai, and Tokyo compete aggressively for the same sector.

The next test: moving beyond pilots

The speech underscored a familiar but unresolved challenge for digital assets: turning technical possibility into everyday financial utility. Faster settlement, better liquidity, and programmable compliance are valuable only if institutions trust the systems, regulators understand the risks, and customers are protected when things go wrong.

For founders and fintech operators in Southeast Asia, MAS’s message is both an opportunity and a constraint. The regulator is open to digital asset innovation, but only where it is tied to clear economic value and strong safeguards. Projects built mainly around speculation will face a colder reception than those improving settlement, treasury operations, compliance, or cross-border liquidity.

Also Read: Tokenised assets have moved on-chain. The liquidity has not followed

That may define the next phase of Singapore’s digital asset market. The city is not trying to be the loosest jurisdiction for crypto activity. It is trying to become the place where regulated digital asset infrastructure can be tested, supervised, and eventually deployed.

Coinbase’s new office gives the company a larger base in that ecosystem. BLOOM gives MAS another channel to shape how digital money develops. Whether either can help digital assets move from controlled pilots to real financial plumbing will be the test that matters.

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For years, online travel has made flights and hotels relatively easy to compare. The messier part begins after touchdown: finding the right train, coach, ferry or local connection, often across operators that do not share booking systems, languages or payment flows.

That gap is where Berlin-headquartered Omio is placing its next big Asia bet.

Also Read: Granite Asia secures over US$350M in first close of private credit fund

The multimodal travel booking platform has secured a US$10 million strategic investment from Granite-Integral, a Japan-focused growth investment platform backed by Granite Asia and Integral Corporation.

The funding will support Omio’s expansion in Japan and help strengthen its presence across Southeast Asia, two regions where international tourism is recovering fast and travellers are increasingly building trips around transport access rather than only destinations.

Omio allows users to search, compare and book different modes of transport, including trains, buses, ferries and flights, through a single interface. Its broader group also includes Rome2Rio, the route-planning platform acquired by Omio in 2019, which is often used by travellers to understand how to get from one place to another when the answer is not as simple as booking a flight.

The company says the new partnership with Granite-Integral will help it deepen transport operator partnerships in Japan and Southeast Asia, improve its product for local and international users, and grow regional teams, including in Japan and at its AI-focused technology hub in Singapore.

Asia’s fragmented travel map

The timing is not accidental. Asia Pacific is expected to be the fastest-growing travel region globally over the next five years, while Omio says nearly half of travellers are now planning journeys that involve more than one mode of transport.

That shift is especially relevant in Southeast Asia, where cross-border and domestic travel often depends on combinations of low-cost flights, coaches, ferries, trains and private transfers. A traveller moving from Singapore to a Thai island, from Ho Chi Minh City to Cambodia, or from Bali to secondary Indonesian destinations may need several operators and separate bookings to complete one trip.

For founders and operators in the region, the opportunity is not just in selling tickets. It is in turning fragmented transport supply into usable digital infrastructure. Southeast Asia has strong demand from backpackers, business travellers, digital nomads and a growing middle class, but its ground and sea transport systems remain unevenly digitised. Many operators still rely on offline sales, agent networks or local booking channels that are difficult for international travellers to access.

That creates room for platforms that can aggregate inventory, simplify payments, and give users confidence that a journey involving multiple legs will actually work.

Japan presents a different but equally complex challenge. Its transport system is among the world’s most advanced, but it is also dense, operator-heavy and sometimes difficult for overseas visitors to navigate beyond the biggest cities. Omio launched in Japan earlier this year and says it is already seeing demand from international travellers booking along the Golden Route, the popular corridor linking cities such as Tokyo, Kyoto and Osaka, as well as trips to mountain regions, sacred routes and coastal communities.

The company has been expanding its Japanese transport network through partnerships with operators, including Japan Railways and Willer Express.

“Millions of travellers visit every year, but planning journeys across different operators and transport modes can still be complex, particularly beyond the major cities,” said Naren Shaam, founder and CEO of Omio. “With Granite-Integral’s regional expertise, we intend to accelerate a new era of connected travel in Japan, expand our presence across Southeast Asia and continue building a more connected future for travel across Asia.”

Why Granite-Integral matters

Granite-Integral brings more than capital to the table. The platform is a joint venture between Granite Asia and Integral Corporation, managing US$100 million in committed capital. Granite Asia has experience investing across Asia Pacific’s technology sector, while Integral brings operational expertise and networks in Japan.

For Omio, that combination matters because transport aggregation is a local relationship business as much as a technology problem. Signing up transport operators, integrating inventory, handling customer support, and navigating local payment preferences all require market-specific execution.

CK Choun, Co-Head of Granite-Integral, said Japan and Southeast Asia represent “some of the most important long-term opportunities in global travel”, with rising demand for more connected journeys across the region. He added that Omio’s platform is built to simplify increasingly fragmented travel experiences.

Omio says it currently offers bookable transport options across 48 countries and serves more than one billion users annually across its platforms. The group sells more than 100,000 tickets daily, employs over 470 people from more than 50 countries, and maintains offices in Berlin, Singapore, Prague, Melbourne and Bangalore.

The company has also set an ambitious target to expand into more than 70 markets worldwide by 2028, with Japan and Southeast Asia named as key priorities.

Rivals chasing the same traveller

Omio is not alone in seeing the value of multimodal travel in Asia. In Europe, Trainline remains a major player in rail and coach booking, while Trip.com Group has deep reach across flights, hotels and rail in Asia, especially among Chinese and regional travellers.

In Southeast Asia, platforms such as Traveloka and Klook have built strong consumer brands around flights, accommodation, activities and transport-adjacent services, while 12Go has long focused on buses, ferries, trains and transfers across emerging travel markets.

Global giants such as Booking Holdings and Expedia Group also have the distribution power to move further into connected trip planning, though transport beyond flights remains a harder category to standardise.

Omio’s challenge will be to prove that it can localise deeply enough while keeping the simplicity that made its European product useful. Southeast Asia, in particular, is not one market. Indonesia’s island geography, Vietnam’s coach networks, Thailand’s tourism corridors and Singapore’s role as a regional hub all require different supply strategies.

Also Read: Granite Asia, Integral form US$100M JV to drive Japan-global tech expansion

If Omio can stitch those pieces together, its regional push could tap into a larger change in travel behaviour: people are no longer only asking where they can go, but how easily they can move once they get there.

That question is becoming central to tourism growth across Asia. For Japan, it could help spread visitors beyond crowded urban routes. For Southeast Asia, it could make secondary destinations more accessible and commercially viable. For Omio, it is the opening it needs to turn multimodal travel from a niche convenience into a mainstream booking habit.

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When Applied Materials brought ASTRA, its Applied Startup Technology & Research Accelerator, to Singapore for the first time this year, it marked more than a regional expansion. It signalled that Southeast Asia’s deeptech ecosystem has matured enough to bridge the gap between breakthrough technology and industrial deployment, a challenge now defining the AI era.

Behind that bet is Applied Ventures, the semiconductor equipment giant’s corporate venture arm, which manages a portfolio exceeding US$400 million across more than 90 companies worldwide.

Also Read: Applied’s US$500M Singapore expansion tightens ties with Southeast Asia’s chip push

In this interview, Abhishek Sud, Senior Investment Director at Applied Ventures, unpacks why Singapore, Taiwan, South Korea, and India are generating the region’s most compelling deal flow, how AI is reshaping capital allocation towards photonics, robotics and energy-efficient computing, and why deeptech investing demands a patience most pure-play VCs simply cannot afford.

ASTRA came to Singapore for the first time this year. Why now, and what about Southeast Asia’s deeptech ecosystem made the timing right?

ASTRA goes where we see the greatest opportunity to accelerate deeptech innovation, and Singapore’s ecosystem has reached the level of maturity that made this the right time. As AI reshapes industries, the challenge is no longer building breakthrough technology; it’s bringing that technology into real-world manufacturing. ASTRA exists to bridge that gap by connecting startups, industry leaders, ecosystem partners and customers.

Southeast Asia has changed considerably in three years: semiconductor investment has grown, engineering capabilities have deepened, and the deeptech startup scene is far more vibrant. Singapore sits at the heart of that momentum, combining world-class research, advanced manufacturing, semiconductor expertise and strong public-private partnerships as a regional hub for Applied Materials’s R&D and commercialisation activities.

Applied Ventures has invested in 18 countries. Where in Asia are you seeing the most compelling deal flow, and which markets have the greatest untapped potential?

We’re seeing strong momentum in Singapore, Taiwan, South Korea and India, where deep expertise in semiconductor and advanced-manufacturing capabilities keeps generating compelling opportunities.

Singapore has become a nexus for innovation and venture activity in Asia, underpinned by a stable, business-friendly tax and regulatory environment, strong legal and financial institutions, and regional connectivity. This is why so many venture firms have chosen to headquarter here. In robotics, companies like Augmentus exemplify the Singapore-based innovation we find compelling.

India is also emerging as an important source of innovation, driven by exceptional engineering talent, an electronics manufacturing base and a fast-growing deep-tech startup ecosystem; companies like VVDN represent this momentum. More broadly, we’re seeing exciting developments across Southeast Asia in AI, robotics, photonics and advanced manufacturing.

As investors, we focus less on geography and more on whether an ecosystem brings together talent, research, manufacturing capability and customer demand. That combination is what lets startups move from breakthrough ideas to real industry adoption.

How does Asia’s deeptech VC landscape structurally differ from the US — in founder quality, exit pathways and corporate willingness to be early customers?

The US has one of the world’s most mature venture ecosystems, with deep capital markets and established pathways to scale. Asia is different — not one market but a collection of innovation ecosystems, each with its own strengths.

For us, the defining characteristic of a strong ecosystem isn’t geography; it’s the ability to turn breakthrough research into real-world adoption. What distinguishes many Asian markets is how closely research, manufacturing and industry are connected. Much of the world’s advanced manufacturing capacity sits in Asia, so founders here often get direct access to foundries, manufacturing partners and strategic customers earlier than they would elsewhere.

Also Read: Deeptech’s secret: Ignore the market, master the engineering, and let opportunity find you

That proximity accelerates how quickly a deeptech company can validate and commercialise its technology, and it’s part of why we’re seeing an increasingly sophisticated pipeline of founders tackling hard problems in AI, semiconductors, photonics, robotics and advanced manufacturing. It also shapes exits, with strategic partnerships and acquisitions by Asian companies playing a prominent role alongside traditional venture outcomes.

You manage a portfolio of over US$400M across 90+ companies. With AI demanding more capital-intensive bets, how are you investing in this AI economy?

AI is influencing where capital flows, but it hasn’t changed our investment philosophy. We view AI as an infrastructure transformation and continue focusing on foundational technologies enabling the next generation of computing: energy-efficient compute, photonics, advanced packaging, thermal management and AI data centre infrastructure, all areas aligned with Applied Materials’s expertise across the materials-to-systems stack.

Beyond digital AI infrastructure, we’re also seeing the emergence of Physical AI, where foundation models converge with robotics, automation and industrial systems, as well as quantum computing and AI for science and engineering. ASTRA Singapore is one way we engage with innovators at this intersection, giving us early insight into emerging technologies.

Our objective is not simply to invest in the current AI cycle, but to identify the technology inflections that will shape the semiconductor and AI industries over the next decade.

Selectivity means more companies don’t get funded. What does that mean for early-stage hardware or materials founders who need early institutional backing?

Greater selectivity raises the bar, especially in hardware and materials, where scaling innovation takes significant time and capital. We look beyond technical novelty to understand the problem being solved and whether there’s a credible path to industrialisation and mass deployment, which is often where our model differs from traditional financial VCs.

Minds.ai and Sigray illustrate this well. Minds.ai applies reinforcement learning and deep learning to optimise semiconductor fab operations; Sigray pioneers synchrotron-grade x-ray systems for materials characterisation.

Both are deeply specialised, hard-tech businesses that traditional financial investors may find challenging to evaluate given their technical complexity and development timelines. We engaged because we understood the problems they were solving and their relevance to the broader semiconductor ecosystem. Over time, both technologies became sufficiently important to Applied Materials’ long-term roadmap that the companies were ultimately acquired.

You’re moving into photonics, robotics and quantum computing, areas with historically optimistic timelines. What’s changed that makes these bets fundable now?

Less has changed than you might expect. Deeptech innovation has always followed the realities of physics, manufacturing readiness and customer qualification, not consumer adoption cycles or market sentiment.

Also Read: Building smart: A tech founder’s guide to the semiconductor supply chain revolution

Photonics illustrates this well. We recognised its potential early through our investment in Ayar Labs and have continued building that thesis as the technology matured, including through Mixx Technologies. As AI infrastructure scales, faster, more energy-efficient data movement is becoming increasingly important.

Similarly, robotics is attracting attention as manufacturing environments become too complex for traditional automation, creating demand for adaptive, AI-enabled systems that handle variability and real-time decision-making — the thesis behind our investment in Augmentus, a Singapore-based robotics AI software company actively engaged with our Worldwide Operations group.

We don’t invest because timelines have suddenly become shorter. We invest when we see technologies reaching an inflection point where advances in science, engineering and market demand begin to reinforce one another.

What threshold separates genuinely transformative energy-efficiency plays from incremental improvements dressed up in the right language?

The real question isn’t whether a technology touches power consumption, but whether it meaningfully improves performance-per-watt at the system level, not just in one isolated component.

Our industry has historically delivered roughly a threefold improvement in energy-efficient performance every two years, largely through transistor- and interconnect-level advances. But at today’s density, leading-edge chips packing hundreds of billions of transistors into an area smaller than a postage stamp, that gain can no longer come from any single innovation working in isolation. It requires advances across logic, memory and advanced packaging: transistor architecture (such as gate-all-around transistors), the interconnects moving signals through dense 3D stacks, and packaging that brings compute and memory closer together to address the “memory wall.”

So, when we evaluate a company, we’re not asking ‘does this reduce power somewhere?’ We’re asking whether it changes the system-level equation.

With deeptech cycles running 10 to 15 years and LPs demanding shorter returns, how is Applied Ventures managing expectations without compromising patience?

Deeptech innovation operates on timelines set by science, engineering and industrial adoption, so patience remains important regardless of market cycles. Investing directly as Applied Materials’s corporate venture arm lets us take a long-term view.

In deeptech, most of the value sits behind a single, high-stakes gate: qualification. Before a novel breakthrough is designed into a customer’s product or process, it generates very little revenue. But once it clears that bar and gets designed into a semiconductor node, manufacturing line or product platform, it tends to stay there for the life of that platform and often carries forward into subsequent generations. That’s why we think of deep-tech value as compounding rather than linear — the payoff is a technology becoming embedded in a customer’s roadmap for years, not a single exit event.

Why should a founder take Applied Ventures’s money over a pure-play financial VC, given corporate VCs can be slower to participate?

What we bring instead of speed is depth. Applied Materials has spent decades building expertise across the materials-to-systems stack, advanced manufacturing and the global semiconductor ecosystem, giving founders access to technical expertise, customers, supply chain partners and co-investors. In some cases, we also become a customer ourselves. Through programmes like ASTRA, startups work directly with our business units on real industry challenges, bridging the gap between a promising technology and commercial adoption.

Also Read: Chips, corruption, and credibility: Malaysia’s semiconductor gamble faces a trust test

That’s particularly valuable in deeptech, where success depends on more than technical breakthroughs. Companies also need to navigate qualification, industrialisation and customer adoption. We help founders through that journey with the technical and commercial support needed to scale.

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Product teams speak a great deal about understanding users, but much of what passes for empathy is still strangely external. A few interviews, some clips from research, a dashboard, a support summary, and perhaps a customer visit if the quarter is going well. The PM learns about the user, but rarely feels anything close to the shape of the user’s day.

There is a meaningful difference between hearing that a workflow is frustrating and living even a diluted version of the conditions that make it frustrating. One gives you information. The other changes your judgment. It changes what you notice, what starts to feel intolerable, and what no longer sounds like a minor edge case once it begins stealing attention from the work that actually matters to the user.

The point is not to imagine the user better

The phrase becoming your user can sound theatrical if handled badly. It can drift into a kind of product tourism, where the PM briefly samples the user’s world and comes back declaring deep insight after a few hours of inconvenience. That is not what I mean.

The goal is not to imagine the user more vividly. The goal is to borrow enough of the user’s constraints that your product judgement changes in useful ways.

That distinction matters because users are not defined only by goals. They are defined by context. What time pressure are they under? What other systems are open? What risks do they carry if something goes wrong? How fragmented is their attention? What language do they think in? Which decisions are reversible and which ones will come back to haunt them in an audit, a manager review, a customer escalation, or a missed operational target?

Good method acting in product is not identity play, it is constraint play

There is an important ethical line here. A PM cannot become the user in any complete human sense. They cannot briefly inhabit a profession, identity, power structure, or lived experience and claim equivalence. That would be shallow and, in many cases, arrogant.

What a PM can do is become answerable to a version of the user’s constraints.

That is the more useful frame. Do not try to imitate the person. Try to inherit enough of the conditions. Work with the same interruptions. Use the same information quality. Accept the same timing pressure. Force yourself into the same system boundaries. Limit yourself to the same training level. Carry the same downstream consequence for delay or error, even if only through a carefully designed simulation.

The most powerful immersion technique is to inherit the user’s compromises

One of the biggest differences between how product teams think and how users behave is that teams think in terms of optimal flows, while users often operate through compromise. They trade accuracy for speed, structure for momentum, local inconsistency for getting the day unstuck, and perfect usage for something that is simply survivable.

A PM begins to understand the product properly when they are forced into the same compromises.

Also Read: Your customers are not buying your product, they are buying a better version of themselves

What do you skip when the system asks too much? What steps do you stop trusting? What notifications do you mentally tune out? Which fields do you fill carelessly because the form has trained you that accuracy is rarely rewarded? Which safeguard do you work around because it arrives at the wrong moment? Which part of the workflow becomes ceremonial rather than meaningful?

This approach improves prioritisation because it changes what feels expensive

One of the quiet failures in product organisations is that teams often price effort correctly and price users badly. They know what engineering work is expensive. They are less precise about what user adaptation is expensive.

Method acting helps correct that.

Once a PM has felt the repeated cognitive drag of a confusing permission model, the hidden embarrassment of a brittle workflow during a customer call, or the compounding irritation of a product that requires too much memory to use safely, they start evaluating product choices differently. What once sounded like a small usability issue starts looking like an ongoing tax on serious work. A feature request that felt secondary becomes strategically important because it removes repeated mental labour rather than adding visible novelty.

Product teams should immerse themselves in pairs, not in isolation

There is also a practical lesson here. A single PM doing an immersion exercise can still turn it into a private epiphany that never quite translates back into the organisation. The better model is paired immersion across functions.

A PM and a designer should take the same support shift together. A PM and an engineer should attempt the same first-time setup under the same constraints. A PM and customer success lead should walk through the same renewal period using only the product and resources a customer would have. Not because cross-functional alignment is fashionable language, but because products fail in layers and different disciplines notice different truths under pressure.

The PM may notice expectation gaps. The designer may notice interpretive failure. The engineer may see state fragility. The customer-facing partner may understand where confidence actually breaks. A shared immersion experience creates a much stronger basis for action than a single insight carried back into a prioritisation meeting.

It also has another advantage. It makes the user’s reality harder for the organisation to sanitise later.

The risk is romanticising pain instead of removing it

There is, however, a failure mode here that product leaders should be honest about. Teams can become fascinated by immersive research and still not change the product in ways that matter. They collect vivid stories, run internal exercises, and leave the user impressed by the company’s curiosity but still burdened by the same avoidable friction.

Also Read: When AI leaves the screen, cybersecurity becomes product responsibility

That is where this whole idea becomes self-indulgent.

Method acting only deserves the name if it leads to stronger action. The purpose is not to feel more empathetic in meetings. The purpose is to remove false assumptions from the product and to design with greater seriousness about what the user’s day actually costs.

If the immersion does not change priorities, defaults, sequencing, onboarding, trust cues, or the burden placed on the user, then the team has performed understanding rather than built from it.

The deepest insight often comes from repeated immersion

There is a tendency in product culture to search for one transformative field visit or one intense customer session that will unlock truth. Reality is usually less cinematic.

The more valuable form of becoming a user is often repeated, almost boring exposure. The PM takes the same support block each week. They use the same constrained setup path every month. They attend the same operational checkpoint at the same stage of the customer cycle. They revisit the workflow during the moments when pressure actually rises.

This matters because many product truths are not dramatic. They are cumulative. A tiny delay repeated forty times. A confusing label that produces just enough hesitation to disrupt pace. A weak default that creates small but constant recovery work. A permission design that nobody describes as broken, yet everyone silently routes around.

Repeated immersion is what reveals these patterns. It turns empathy from a moral gesture into an operating discipline.

—

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

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Singapore’s data professionals are proving to be among the most cautious in the world when it comes to letting artificial intelligence (AI) operate unsupervised.

According to a new global study, 61 per cent of the city-state’s data analysts prefer a human-in-the-loop approach to AI, the highest share recorded across all regions surveyed.

Also Read: AI in Singapore: From generative tools to real-world impact

The finding comes from Alteryx’s “2026 State of the Data Analyst: The Rise of Business Logic” report, which polled 1,400 respondents worldwide, including 175 data analysts and IT leaders in Singapore. It paints a picture of an ecosystem that is deploying AI aggressively, yet remains deeply uneasy about handing it the keys.

Just 1 per cent of Singapore respondents said they were comfortable with AI operating fully autonomously, a third of the already-slim 3 per cent global average. For a market that has positioned itself as Southeast Asia’s AI testbed, courting hyperscaler data centres and government-backed AI programmes, the reluctance is telling. It suggests that enthusiasm for AI adoption at the policy and infrastructure level has not necessarily trickled down into trust at the operational level, where analysts are the ones left cleaning up after the models.

Strategic weight is rising, but so is the workload

The report does not describe a market turning its back on AI. Quite the opposite — 75 per cent of Singapore respondents said AI’s strategic impact on their organisation has grown over the past year, as companies lean further into automation and agent-based systems. And 66 per cent agreed that AI and agent-based systems perform best when managed at the business-unit level rather than by centralised data or IT teams, a preference that echoes a broader shift happening across the region, where domain teams increasingly want ownership over the tools shaping their decisions, rather than waiting on a central function to translate their needs.

That shift, however, is generating friction of its own. Singapore’s analysts are spending significant chunks of their working week doing the unglamorous groundwork AI still cannot do reliably alone: an average of five hours a week preparing and cleaning data, and a further three hours correcting and validating AI-generated outputs. Put together, that is roughly a full working day each week spent making sure the machine’s homework is actually right.

Data quality, not the models, is the real bottleneck

Perhaps the most pointed figure in the report is this: 46 per cent of AI and analytics projects in Singapore that fail to meet their objectives are attributed primarily to data-related issues, rather than problems with the underlying models or tooling. In other words, the technology is rarely the weak link; the data feeding it is.

Also Read: Singapore turns AI scrutiny towards chatbots, personal data, and digital twins

This tracks with a pattern seen repeatedly across Southeast Asia’s broader digitalisation push, where legacy systems, fragmented data ownership across departments, and inconsistent data hygiene practices have quietly undermined more ambitious AI rollouts. Singapore, despite its relatively mature digital infrastructure compared with regional peers, is not immune.

The report also flags governance as a growing pain point sitting alongside the data quality problem. Thirty-seven per cent of respondents cited data quality issues as a leading source of friction when deploying AI, while 38 per cent pointed to data access approvals, the bureaucratic back-and-forth of getting the right people cleared to use the right datasets. Meanwhile, 46 per cent said unclear ownership and accountability for AI-driven decisions is a barrier standing between generating an AI insight and actually being able to act on it.

Taken together, the figures describe an organisational bottleneck as much as a technical one. Companies can buy the AI tools, but if nobody is clearly responsible for the decisions those tools inform, and if data access still requires multiple rounds of sign-off, the promised speed gains from automation start to erode.

“Business logic” as the missing layer

Philip Madgwick, Alteryx’s regional vice-president for Asia, framed the findings around a familiar tension: the gap between deploying AI and actually trusting what it produces. He said many organisations in Singapore are still contending with poor data quality, weak governance and lingering uncertainty over AI-generated outputs, and that what separates companies that pull ahead from those that stall is whether the people closest to the business are the ones defining and managing the logic behind AI’s decisions.

That framing matters for how Southeast Asian companies think about their next phase of AI investment. Much of the region’s AI narrative over the past two years has centred on adoption velocity: how quickly enterprises can bolt generative AI or agentic systems onto existing workflows. Alteryx’s data suggests the more pressing question for 2026 is not how fast organisations can deploy AI, but whether they have built the underlying data foundations and accountability structures to actually trust what it outputs.

Why it matters for the region

Singapore’s caution here is worth watching precisely because of its outlier status. As a market often seen as further along the AI maturity curve than its Southeast Asian neighbours, its analysts’ reluctance to cede control signals that the “trust gap” between AI capability and AI governance may not simply close with more advanced tooling or bigger budgets.

Also Read: Singapore’s AI tools are ready. Its workforce isn’t

If anything, the report suggests that as agentic systems become more capable and more embedded in day-to-day business decisions, the human-in-the-loop instinct may harden rather than fade, with organisations that invest early in data governance and clear decision ownership best placed to convert AI adoption into genuine business results.

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Balaji Srinivasan has never lacked for big ideas. The former Coinbase CTO and Andreessen Horowitz General Partner has spent years evangelising the “network state”, a borderless, internet-native community that eventually negotiates diplomatic recognition from existing governments.

In late 2024, that theory got a physical address: Network School, a pop-up village of founders, engineers, and crypto enthusiasts housed inside the half-empty towers of Forest City in Johor, Malaysia.

Also Read: Six practical tips on how to become a digital nomad

Nine months later, Malaysian authorities tightened the valve considerably. Immigration officials began scrutinising the visa status of participants, several of whom were reportedly operating on tourist or social visit passes rather than legitimate work or student permits. Attendees describe increased checks, warnings, and mounting uncertainty over whether the programme can legally continue as advertised.

For a region that has spent a decade courting digital nomads and “future of work” narratives, the crackdown poses an uncomfortable question: was this inevitable, or did Malaysia fumble an opportunity?

A school that wasn’t quite a school

Part of the problem is definitional. Network School was never registered as an accredited institution, nor did it fit Malaysia’s existing frameworks for co-working visas, MM2H, or the DE Rantau digital nomad pass. It was designed to be something new — part co-living space, part ideological experiment, part unofficial curriculum on governance and technology.

That ambiguity is the entire point of a network state: operate in the gaps between existing legal categories until enough legitimacy accrues to demand its own. But immigration officers do not deal in theory. They deal in passport stamps and one blunt question: is this person working, studying, or just visiting? When hundreds cycle through a facility for months at a stretch, ambiguity stops being clever. It becomes a compliance risk.

Seen this way, the crackdown isn’t hostility to innovation. It is a government enforcing the line between visitor and resident, a line every country, Singapore included, guards jealously.

The bigger tension: Ideology meets sovereignty

Still, something here deserved better handling. Forest City, the ghost-town mega-development built by China’s Country Garden, has been desperate for any sign of life since its US$100-billion vision collapsed under oversupply and bad geopolitical timing. Whatever one thinks of its ideology, Network School brought paying occupants and foreign attention into towers that had sat empty for years. A calibrated response — clear guidance, a bespoke visa category, a formal MOU — could have captured the upside while closing the compliance gap.

Instead, ambiguity was left to fester until it became an enforcement story instead of a policy one. That is a familiar Southeast Asian pattern: digital nomad experiments get welcomed in speeches, while the bureaucratic plumbing to support them lags years behind the marketing.

There is a sharper ideological discomfort too. Network State philosophy, at its most provocative, imagines communities that eventually seek sovereignty or special jurisdiction,  a proposition no nation-state, least of all one still nursing a foreign-funded property collapse on its own soil, should wave through without scrutiny. Malaysia is right to ask who governs Johor’s newest experiment, and under whose laws.

A middle path still exists

None of this means the door should close for good. Johor’s ambitions, anchored by the Johor-Singapore Special Economic Zone, depend on attracting exactly the kind of mobile, high-value talent Network School claims to cluster. The lesson isn’t that experimental communities are dangerous. It’s that experiments need a legal home to grow into, not just a rented tower and a manifesto.

Also Read: SPUN raises US$1.8M to fix SEA’s broken visa infrastructure

For founders and investors watching from Singapore, Jakarta, or Bengaluru, this episode is a reminder that Southeast Asia’s openness has limits, defined by sovereignty, labour law, and public accountability. The most interesting network states of the future won’t be the ones that dodge these questions. They’ll be the ones that answer them well enough to earn a seat at the table. Johor still has time to write that invitation. It would be a shame if bureaucratic inertia, not genuine disagreement, was the reason it never got sent.

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For years, corporate innovation in Southeast Asia often meant a hackathon, an accelerator demo day, or a small pilot that rarely survived the next budget cycle. Singapore is trying to push the model into something more durable: corporate venturing as a repeatable business capability, not a branding exercise.

That is the central argument of a new joint publication by global management consultancy Arthur D. Little and the Singapore Economic Development Board (EDB), titled “Singapore As A Global Platform For Corporate Venturing”. The paper examines how Singapore’s mix of multinational corporations, startups, research institutions, investors and public-sector support can help companies build new products, enter adjacent markets and commercialise emerging technologies faster.

Also Read: How corporate innovation in Vietnam is fledgling the B2B startup ecosystem

The timing is not accidental. Across Southeast Asia, large companies are under pressure from faster technology cycles, shifting supply chains, AI adoption and a more selective funding environment. Startups, meanwhile, are finding that access to corporate customers, distribution and technical validation can be as valuable as capital. Corporate venturing sits at that intersection.

In simple terms, corporate venturing refers to the ways established companies work with startups or build new ventures themselves to access innovation beyond their internal research and development teams. This may involve startup partnerships, venture building, co-development, pilots, minority investments or commercial spin-outs. Done well, it gives corporations a way to test new ideas without betting the entire organisation on them.

From innovation theatre to business discipline

The ADL-EDB publication argues that corporate venturing is becoming more important because traditional growth playbooks are no longer enough. Acquisitions can be expensive and slow. Internal R&D can be too insulated from market feedback. Partnerships without ownership or governance can stall after an initial trial.

By contrast, structured corporate venturing allows companies to identify emerging technologies, run pilots in real-world settings, validate market demand and scale successful ideas through established commercial channels.

“Corporate venturing is no longer an organisational initiative, it is a core strategic capability for companies seeking to stay competitive in the rapidly changing innovation landscape,” said Daniel Chow, Principal at Arthur D. Little Singapore.

That distinction matters. Many large organisations in the region have experimented with startup engagement, but fewer have built the internal machinery needed to turn experiments into business outcomes. The study highlights five requirements: access to emerging technologies, faster validation through pilots, clear governance and ownership, ecosystem partnerships that reduce execution risk, and portfolio-based innovation management.

Also Read: Why it maybe the opportune time to consider Corporate Venture Capital

The last point is especially relevant in Southeast Asia, where market fragmentation can make scaling difficult. A product that works in Singapore may need different pricing, regulations, logistics or customer education in Indonesia, Vietnam, Thailand or the Philippines. Corporate partners can help startups navigate these differences, but only if collaboration goes beyond a press release.

Why Singapore is leaning into the model

Singapore’s pitch is that it can serve as a controlled launchpad for corporate-startup collaboration before companies expand regionally. The city-state has long used its position as a headquarters hub to draw multinational corporations, capital and talent. The publication notes that Singapore is ranked as the most popular regional headquarters destination in Asia, giving it an unusual density of decision-makers for a market of its size.

Its startup ecosystem has also climbed sharply, rising from 16th globally in 2020 to fourth in 2025, according to the publication. That rise reflects years of public investment in research and innovation, stronger university-industry links, and a deepening pool of founders, venture investors and technical talent.

EDB’s Corporate Venture Launchpad is one example of how the government has tried to institutionalise this activity. The programme supports companies in building new ventures from Singapore, often by pairing corporate assets with entrepreneurial teams and market validation processes.

“This report reflects the growing momentum of corporate venturing across Singapore’s business community, especially in AI-enabled growth sectors such as advanced manufacturing, healthcare, semiconductors, and the digital economy,” said Joseph Tay, Vice President and Head of Innovation Strategy and Partnerships at EDB.

Also Read: AI in Singapore: From generative tools to real-world impact

These sectors are not chosen at random. Advanced manufacturing and semiconductors are tied to Singapore’s role in global supply chains. Healthcare and biomedical sciences build on the country’s research base, hospitals and regulatory credibility. AI and the digital economy cut across nearly every industry, from financial services and logistics to drug discovery and factory automation.

The Southeast Asian relevance

For the wider region, Singapore’s corporate venturing push could have effects beyond its borders. Many Southeast Asian startups use Singapore as a funding, headquarters or enterprise sales base while operating in larger neighbouring markets. If more multinationals and regional conglomerates build structured venturing teams in Singapore, startups could gain better access to paid pilots, technical expertise and cross-border commercial opportunities.

This is particularly important in the current funding climate. After the excesses of 2021, investors have become more disciplined, and founders are under pressure to prove revenue quality, not just user growth. Corporate partnerships can help bridge that gap, but they can also be slow, bureaucratic and difficult to convert into meaningful contracts.

That is why governance matters. A common failure point in corporate-startup collaboration is the absence of a clear business owner. A startup may impress an innovation team but fail to secure support from procurement, legal, compliance or the operating unit that actually owns the problem. The ADL-EDB publication’s emphasis on ownership and commercialisation pathways is a recognition that innovation must eventually survive inside the corporate machine.

Singapore may have advantages here, including strong legal infrastructure, regulatory clarity and proximity to regional headquarters. But it also faces competition from other Asian hubs. Japan and South Korea have large corporate balance sheets and deep technology sectors. India offers scale, software talent and a thriving startup market. China remains a major centre for hardware, manufacturing and AI application, despite geopolitical complexities.

Singapore’s differentiation is less about domestic market size and more about orchestration. It can convene corporates, startups, universities, investors and regulators in a compact ecosystem. The challenge is ensuring that this orchestration produces companies and products that scale beyond Singapore.

What comes next

The publication by ADL and EDB is not a market-moving announcement by itself. It does, however, reflect a broader shift in how Singapore wants to position itself in the next phase of innovation: not merely as a place where startups raise money, but where corporations build new growth engines with startups and research partners.

Also Read: AI meets IP: Why Singapore is the launchpad for AI-driven startups

For founders, that could mean more opportunities to work with enterprise customers earlier. For corporates, it raises the bar: startup collaboration can no longer sit at the edge of the organisation, disconnected from strategy and profit-and-loss responsibility.

The real test will be whether more of these ventures move from pilot to procurement, from experiment to revenue, and from Singapore launchpad to Southeast Asian scale. If they do, corporate venturing may become less of an innovation buzzword and more of a practical route to building the region’s next generation of technology businesses.

The post Singapore’s startup rise puts corporate venturing in sharper focus appeared first on e27.

For years, startup success was often measured by visible growth.

A bigger office. More departments. More employees.

As founders, many of us dreamed of building the next great tech company, and somewhere along the way, headcount became a proxy for success. Every new hire felt like validation that the business was moving in the right direction.

I used to think that way too.

When I built my first SaaS company, People’s Inc., my focus was on growing the team. We hired across sales, marketing, design and operations because that was what successful companies were supposed to do. But as the company expanded, I realised I was spending less time building the business and more time managing it.

The lesson wasn’t that hiring was wrong. It was that I had been optimising for the wrong metric.

Today, I believe AI is forcing founders to rethink one of entrepreneurship’s oldest assumptions: bigger companies are not necessarily better companies.

The businesses that thrive in the next decade may not be the ones with the largest teams. They may simply be the ones with the greatest leverage.

Growth creates complexity, not just capacity

Hiring more people certainly increases what a company can accomplish. It also introduces something less obvious: management overhead.

Every additional hire brings communication, coordination, onboarding, alignment and accountability. Decisions take longer because more people need context. Small misunderstandings can become expensive problems.

One of the hardest lessons I learnt as a founder was that, even if you aren’t personally involved in every conversation, you remain responsible for the outcome.

Also Read: Singapore’s data analysts trust AI to work, not to think

A message delivered differently than intended. A decision interpreted incorrectly. A relationship that breaks down because of poor communication. As founders, those responsibilities always come back to us.

That isn’t a criticism of teams. It’s simply the reality of leadership. As organisations grow, complexity grows with them.

AI changes what founders should optimise for

The conversation around AI often focuses on replacing jobs or reducing costs. I think that’s the wrong conversation. AI isn’t valuable because it replaces people. It’s valuable because it changes how human time is spent.

When I built Seraphina, my AI chief of staff, I approached it very differently from how I had previously built companies. Instead of asking, “Who should I hire next?”, I started asking, “Does this task actually require a human?” Many tasks don’t.

Scheduling meetings. Managing reminders. Coordinating workflows. Retrieving information. Organising knowledge. Following up on repetitive administrative work. These activities are essential, but they don’t necessarily require uniquely human judgement.

By allowing AI to handle these operational tasks, the people on my team have more capacity to focus on work that creates disproportionate value. Not because AI is cheaper. Because human attention is more valuable.

The future workforce isn’t smaller, it’s more focused

This doesn’t mean businesses won’t need employees. Restaurants still need chefs and service staff. Healthcare still depends on doctors and nurses. Manufacturers still require skilled operators. Every industry will adopt AI differently.

But even in businesses that will always rely heavily on people, the nature of work is changing.

The most valuable employees won’t simply execute processes. They’ll build relationships. They’ll earn trust. They’ll negotiate. They’ll think strategically. They’ll solve problems creatively.

Those are capabilities that become even more valuable when repetitive execution is increasingly handled by intelligent systems.

People still buy from people

Customers may interact with AI assistants, receive AI-generated recommendations or automate parts of their buying journey. But trust, conviction and long-term relationships remain deeply human.

That is where founders should invest their teams.

Also Read: AI-powered business automation: How SMEs are transforming operations in Southeast Asia

Revenue should come before headcount

One mindset shift has shaped how I build businesses today.

I no longer believe founders should hire simply because they’re growing. They should hire because a human creates value that technology cannot.

That changes the order of operations.

For decades, the startup playbook looked something like this:

• Raise funding.
• Hire aggressively.
• Build the organisation.
• Then chase growth.

Increasingly, AI allows founders to reverse that sequence.

• Validate demand.
• Generate revenue.
• Build systems.
• Automate repetitive work.

Then hire intentionally where human expertise creates the greatest impact.

For founders building software, micro-SaaS businesses or digital-first companies, this shift is particularly powerful. A small, focused team equipped with AI can often accomplish what previously required significantly more people.

In my own work building Seraphina, we’ve been able to grow the platform while keeping the organisation deliberately lean. Today, the product generates revenue while remaining focused on systems, automation and thoughtful hiring rather than expanding headcount for its own sake. It’s also a philosophy I’ve discussed with founders, where the biggest transformation is rarely learning another AI tool. It’s learning how to redesign the way a business operates.

The next generation of founders may build differently

For years, entrepreneurs celebrated companies with hundreds or even thousands of employees because that represented scale. In the AI era, scale may look different. It may be measured by how much value each person creates rather than how many people sit on the payroll.

The founders who succeed won’t necessarily be those who build the biggest organisations. They’ll be the ones who build the strongest systems. The ones who use AI for execution, people for judgement, and processes to connect everything together.

Ultimately, entrepreneurship has never been about collecting employees. It’s about creating value. AI doesn’t change that goal. It simply gives founders a new way to achieve it.

—

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 end of headcount as a success metric: How AI is redefining scale appeared first on e27.