Founders still ask me the question they asked in 2019: what does a good backlink cost. This September I finally answered it properly. ESBO Ltd, the link building and digital PR agency I run, exported its entire publisher database, 16,625 sites across 53 languages with verified authority scores and traffic, and published the numbers as the State of Link Building and Brand Mentions 2026. The median sponsored article costs US$570. That turned out to be the least interesting thing in the data.

The publishers who charge most sell no equity

One publisher in ten sells nofollow links only, meaning links that search engines are explicitly told not to count. Move up the authority ladder and the share climbs: at a Domain Rating of 80 and above, the top tier of site authority, 39 per cent of publishers are nofollow only, and among sites with more than a million monthly visits, 36 per cent. Those publishers quote a median of $1,840 per article. Everyone else quotes US$510.

I expected the opposite when I ran the numbers. Instead the pattern is clean: 3.6 times the price for placements that pass none of the search equity buyers think they are paying for. Large publishers do not sell link equity. They sell their audience and their name, and they price that as advertising, with the hyperlink as a formality.

Also Read: The founder-to-minister pivot isn’t the problem, ASEAN’s missing governance infrastructure is

The machines already agree with them

Two public data sets explain why that pricing now makes sense. Ahrefs tested which factors correlate with a brand’s visibility in Google’s AI Overviews across 75,000 brands, and branded web mentions correlated at 0.664 against 0.218 for backlinks. On that measure, being talked about predicts AI visibility about three times better than being linked to. Muck Rack, analysing more than 25 million links cited by ChatGPT, Claude and Gemini, found earned media accounts for 84 per cent of AI citations, and paid or advertorial content for 0.3 per cent.

Put those together and a sponsored placement in 2026 buys three separate things: an audience that reads it, a brand mention in a context machines index, and sometimes a link. The market has repriced from the top down, and it is the mechanism under everything I have written in this column since February about becoming the source machines quote.

The Southeast Asia discount, and the trap inside it

Now the part that matters for this region. English is not one market in the data. English-language sites whose readers sit mainly in Western countries carry a median price of US$593. The 1,697 English sites whose readers are mainly in South and Southeast Asia or Africa: US$150. Sites read mostly from India: US$100.

Read one way, that is a genuine buying opportunity. A startup selling into this region can appear in front of its actual buyers for a quarter of Western prices, and the same gap runs through local languages, where Central and Eastern European placements cost half of what Western European ones do.

Also Read: Taiwan bets on Gen Z founders to move beyond its chip-supplier image

Read the other way, the cheap end is where the trap sits. Disclosure collapses as prices fall. Estonian publishers state a sponsored label 83 per cent of the time, Indonesian publishers 9 per cent. And if machines cite paid content 0.3 per cent of the time, a US$60 undisclosed link on a site nobody reads buys neither search equity nor machine memory. It buys a line in a report.

Spending a small budget like it is 2026

Four adjustments follow for a founder with modest money.

Buy the audience and the mention, not the metric. A US$300 placement whose readers are your actual buyers beats a US$900 one chosen for its authority score.

Treat the link attribute as a bonus. If the article is worth publishing with a nofollow link, it is worth publishing. If it only makes sense followed, you are buying the wrong thing.

Move the saved money to earned coverage. Journalists, reviewers and industry newsletters generate the 84 per cent, they cost effort rather than invoices, and publishing original numbers about your market is still the fastest way to interest them.

Measure mentions. Most teams still count referring domains. Start counting how often your name appears, per market, in contexts machines read, because that is the number moving your AI answers.

The price lists are telling founders something the industry took years to admit. The most sophisticated publishers quietly stopped selling links some time ago. They sell being known, and that is the part the machines keep.

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Most AI writing about work still frames the shift too narrowly. The conversation often starts with a familiar question: which tasks can AI do? That is a useful starting point, but it misses the more important operational change.

The deeper shift is not that AI can write, summarise, classify, or draft faster than before. It is that AI is starting to sit inside the flow of work itself. It can monitor inputs, prepare actions, surface missing information, follow rules, ask for approval, and help move a recurring job forward. That does not make it an employee in the literal sense. But it does change what humans need to manage.

This matters because most work does not break at the level of one isolated task. It breaks when the next step is unclear, the wrong person owns the follow-up, the context is scattered across tools, exceptions are missed, or a decision goes out before it has been checked. In other words, work often fails in the operating layer between tasks.

That is where AI is becoming more consequential.

A support team does not only need a draft reply. It needs the issue summarised, the account context pulled together, the relevant policy found, the refund rule checked, the risky cases flagged, and the final action paused for review when money or commitments are involved. A sales team does not only need notes cleaned up. It needs lead context structured, missing information identified, the next step drafted, the CRM updated, and stale leads surfaced before they disappear. A finance team does not only need extraction. It needs exceptions separated from routine items, approvals routed correctly, and anything customer-facing or irreversible held back until someone signs off.

These are not just tasks. They are managed responsibility systems.

That is the more useful way to understand the shift. AI is not only helping people complete individual actions. It is helping organisations redesign how recurring responsibilities are carried, checked, and escalated.

This is also why the “AI employee” framing can mislead. An employee is not just a bundle of outputs. An employee sits inside accountability, authority, escalation paths, and consequences. Most organisations are nowhere near handing all of that over. What they are doing instead is more specific and more realistic: they are letting AI prepare, route, monitor, and sometimes execute bounded steps inside a workflow, while humans remain accountable for judgment and exceptions.

That distinction matters.

Also Read: AI won’t just replace jobs. It will redesign how companies work

When companies treat AI like a smart drafting box, the human still carries nearly all of the responsibility. The person has to remember what to do, gather the context, issue the prompt, inspect the output, send the result, and remember to follow up later. The AI helps with one segment of the work, but the burden of orchestration remains largely human.

Once AI is connected to tools, triggers, records, and approval points, the shape changes. The system can notice a new request. It can compile the relevant context. It can draft the likely next action. It can flag missing data. It can ask for approval before a financial, legal, or customer-facing step is taken. It can record what happened and surface what still needs attention.

The responsibility does not disappear. It gets redistributed.

That redistribution is where many teams are still underestimating the management challenge. As AI moves closer to action, organisations need clearer decisions about where approval is required, which actions are reversible, what should always stay human-led, and what evidence the system should attach before asking for sign-off. A bad draft is one problem. A bad action wrapped in a polished draft is another.

This is why operational design matters more than prompt cleverness. The hard questions are not only about what model to use. They are about what should trigger the workflow, what context should be assembled automatically, what counts as a routine case, what should pause for review, where the result should be stored, and what should happen when the system encounters ambiguity.

Also Read: Your startup has an AI strategy. Does it have a human strategy?

These questions sound mundane compared with product demos. They are also much closer to how work actually succeeds or fails.

That has strategic implications for hiring and management too. As more recurring work is repackaged into supervised AI systems, strong individual contributors will need to think more like operators. They will need to define decision points, identify failure states, design escalation paths, and describe what good output actually looks like in context. The value shifts away from doing every small step manually and toward designing how those steps should move.

This does not mean every team should rush into deep automation. Some workflows are too messy. Some decisions are too sensitive. Some domains generate too much downstream risk if the system acts too early. In many cases, the best design is not full autonomy but staged assistance: prepare the context, draft the recommendation, require approval for the critical move, and keep a visible record.

That may sound less dramatic than the idea of AI becoming an employee. But it is probably closer to what serious adoption will look like inside real organisations.

The next phase of workplace AI is not just about replacing effort. It is about redesigning responsibility.

That is the real management shift. The question is no longer only which task AI can perform. It is which recurring responsibility can be turned into a supervised system with clearer triggers, better context, tighter review, and fewer dropped handoffs.

That is where AI starts changing work more deeply.

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AI technology is transforming the agricultural industry, changing how the outside world views farming and creating a data-driven system that ensures precision and maximises yields.

Imagine a world where technology tells you remotely how many nutrients are lacking and how plants are showing signs of stress and so on. It’s a beautiful experience, not science fiction like many thought. It’s a reality being practised by some big farms around the globe.

Ways in which AI is transforming agriculture

• AI makes caring for every plant and animal less laborious

For centuries, farmers have relied on just walking around the farm field to take records of animal and plant performance. Oftentimes, it is extremely challenging to manually care for every single plant and animal, especially for commercial farmers. Data and images of farm activities are seamlessly fed into AI systems via drones, satellites, and ground sensors. Making it much easier for farmers to remotely spot problems that the human eye could struggle with. For example, early disease detection, nutrient shortages and water stress control. In fact, it’s like having a health app that swiftly informs you before trouble spreads. 

• AI helps farmers to know when and how much to feed and water plants and animals

As important as water, feed, supplements, and fertilizers are, excessive application can cause other problems. AI-powered soil sensors, weather data, and smart irrigation and feeding systems can help farmers figure out how much each part of the field needs (either plant or animal) and when they need it. Typically, this helps farmers to save money by controlling waste of resources. 

• AI helps farmers to solve the weather and planting season puzzle

Accurate weather prediction has been a long-standing issue for farmers. AI can turn farmers’ “gut feeling” into accurate predictions on when to plant, what kind of crop to plant, reveal dry and rainy seasons and lot more. Over the years, making predictions has been a struggling art for farmers, but AI makes it seamless as it digests years of weather records, market trends, and soil data to give farmers a clearer recommendation. Of course, this technology is not at its perfect state yet, but it’s a huge step-up in agriculture.

• AI takes away the back-breaking tasks from farmers’ shoulders

For decades, farmers have been relying on human labour for tasks like weeding, planting and harvesting. Typically, these tasks are super stressful, back-breaking, and even expensive. However, well-designed technological farm tools like robotic weeders, autonomous tractors, smart harvesters, and so on that are guided by artificial intelligence (AI) fill the gap. They work longer hours without getting tired. Large farms that are short on labour can leverage it for their seasonal operations.

Also Read: Why Southeast Asian agritech must build for acquisitions, not IPOs

Core challenges to look out for

The promise of this technology is very real and enticing: better yields, much less waste, making smarter decisions and a lot more, but the everyday realities of farmers make it uncertain and a lot more challenging for many farmers to align with it. Here are major relatable hurdles to look out for;

• High initial investment cost and uncertain ROI

According to Mckinsey’s Global Farmers Insight in 2024, one of the major barriers to agricultural technology is high cost. This research revealed that European and North American countries are leading in global agricultural technology adoption. Meanwhile, about 52% of North American and 48% of European Farmers cited “huge costs” as the biggest challenge of adopting agtech; and about 40% of North Americans also reported that “unclear ROI” stands as a huge barrier to adoption.

Compared to large agribusiness farmers operating in millions of hectares, small and mid-size farms feel this the most. It’s very difficult for small or mid-sized farms to spend thousands of dollars on drones, sensors, software subscriptions, and a lot more with uncertain ROI. In fact, most of these farmers need clearer proof that their investments would pay off before investing in any seasonal budget. 

• Displacement of human labourers

In rural and regional communities where farms have adopted AI automation, the displacement of human labour will be high because the machine can run human operations for hours without getting tired, and as such, there won’t be a need for extra labour to attract extra cost. However, there is a growing need to have skilled personnel to operate those machines excellently.

• Network, power and connectivity barrier in rural areas

Here is another crucial barrier to look out for. From all indications, almost every AI-powered tool needs a stable network supply to enhance seamless data communication. And all devices need electricity to operate efficiently.

This is a roadblock for rural and regional farmers where the network is completely unreliable. Without a stable network, soil sensors can’t communicate to the cloud, apps wouldn’t be able to pull weather models, and even cameras won’t be able to spot pests in real time.

• Operational knowledge complexity

Farmers who are not familiar with sophisticated devices would find it daunting to operate AI tools. The language barrier ( to read through the manual), the huge numbers of low literacy within those regions and the limited number of training they might receive make it very challenging to adopt.

• Data privacy and trust struggle

Here is another barrier you can’t ignore, as the success of AI tools depends on their ability to learn from large numbers of datasets accumulated from the farm. But the big worry is always where this data is stored, how accessible the data is to farmers and a lot more. Meanwhile, there are farmers who are intimately accustomed to their farms such that they feel it’s unsafe to share sensitive data with AI. Imagine a device telling you when to irrigate, feed or apply fertiliser without explaining why; this makes some farmers feel like they are handing control to strangers.

Also Read: Agritech’s next business model may not charge the farmer

AI advancement in technology and how it’s helping farmers today

According to futurist Jim Carroll, AI advancement in agriculture offers many promising pathways in both crop and animal production. It’s already delivering exciting benefits like boosting yields, reducing waste, and improving animal welfare. Moreover, fascinating agricultural technology companies are tirelessly working to improve farming across the globe. Here are a few;

• Inventions towards targeted weed control/precision spraying with strong global recognition are John Deere See & Spray and Carbon Robotics LaserWeeder: They are advanced computer-vision and machine learning precision agricultural AI systems that can swiftly identify the target(weed) in real time and spray only them, not the entire field
• Invention towards crop monitoring, disease and pest detection: Taranis and Plantix are high-tech inventions that are AI-powered for early detection of pests and diseases, nutrient deficiencies and more.
• Invention towards advisory chatbots and smallholder tools: Farmer.Chat and Kisan e-Mitra are AI chatbots that help farmers access information on schemes, weather, pest and disease management, and more. They are often used in Africa.

In conclusion, AI is already being used on real farms, and it’s transforming farm activities from constant worries to something smart. While its primary goal is to help farmers grow more food with less waste, fewer chemicals, and almost no guesswork, it doesn’t mean farming suddenly becomes easy.

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For many people, foot pain is ignored until it starts changing everyday behaviour. They walk less, avoid stairs, switch shoes, or live with knee and back discomfort that seems unrelated. GoodARCH is betting that a five-minute scan can narrow the gap between early signs and medical attention.

The Asian arch support brand, operated by Taiwan-based Homeway Technology, has invested nearly US$230,000 to develop an artificial intelligence-powered foot mapping system, which it is now introducing in Malaysia. The system generates a personalised foot assessment in about five minutes, using image recognition and footprint mapping to help users understand their arch structure and spot imbalances earlier.

Also Read: From smart rings to health coaching: AI and the new preventive healthcare paradigm

The technology is available at several locations, including GoodARCH’s headquarters on Jalan Ampang in Kuala Lumpur, as well as in Penang, Batu Pahat and Johor Bahru. The company said it also plans to work with local health management providers as it expands in the country.

The launch sits at the intersection of two trends that are becoming more visible in Southeast Asia: consumers taking a greater interest in preventive health, and wellness brands using AI to turn quick assessments into personalised recommendations. In markets such as Malaysia, where private healthcare costs are a concern and an ageing population is putting more pressure on the health system, tools that help people identify potential issues earlier are drawing growing commercial attention.

From arch support to AI assessment

GoodARCH was founded in 2002 by Dr Hsieh Chin-Hsing. It began with arch support products before expanding into health footwear and everyday wellness support. Its latest AI foot mapping system was developed over 12 months by an 11-member team spanning medical engineering, business and edge computing.

According to the company, the system builds on its earlier use of cloud-based image recognition and reusable silicone footprint mapping. In practice, the pitch is straightforward: users receive a quick assessment of their foot structure, which can then inform the choice of arch support or related products.

GoodARCH said its technology has so far supported foot structure assessments for more than 300,000 users. Each assessment is paired with its Far-Infrared Arch Support insole, which the company says is designed for stability and shock absorption.

“GoodARCH has continued to invest in foot health technology and research, evolving from infrared-based arch support solutions to graphene technology, proprietary Torsion Field Energy technology, and now AI-powered digital foot mapping,” said GoodARCH Chairman Hsieh Ming-Chia.

The company has also extended its materials and wellness technologies into Health Rhythm, a physiotherapeutic recliner designed to support circulation and sleep through a passive routine. Its core technologies have received medical device approvals and National Quality Award certification in Taiwan, and it holds patents in markets including Malaysia, mainland China, Hong Kong, South Korea, the Philippines, Thailand and Indonesia.

Why Malaysia matters

Malaysia is a logical testbed for GoodARCH’s next phase. The country has a relatively developed private healthcare and wellness market, urban consumers familiar with mall-based health screenings, and a growing middle class willing to spend on products that promise comfort, mobility and long-term wellbeing.

Also Read: The US$500 fix that could unlock a lifetime: How MiracleFeet is closing Asia’s clubfoot gap

There is also a real public health backdrop. GoodARCH cited data suggesting that up to 75 per cent of people will experience a foot problem during their lifetime. Separately, a study of 190 students by the International Islamic University Malaysia found that 26.3 per cent had flat feet.

Flat feet do not always cause pain, and not every case requires treatment. But arch structure can influence balance, gait and load distribution across the body. Over time, persistent misalignment may contribute to discomfort in the ankles, knees, hips or lower back, particularly among people who stand for long periods, wear unsuitable footwear, or have age-related joint problems.

Malaysia’s ageing population makes this more relevant. Data from the Ministry of Health Malaysia and the Malaysian Orthopaedic Association show that 30 to 40 per cent of Malaysians aged 60 and above suffer from knee osteoarthritis. While osteoarthritis has multiple causes, including age, weight, genetics and injury, long-term biomechanical stress can be a contributing factor.

For GoodARCH, the opportunity is not to replace clinical diagnosis but to make the first step less intimidating. “Many people only start paying attention to their feet when pain or difficulty walking begins to affect daily life. AI gives us an opportunity to change that by making foot assessment a simpler first step toward greater awareness and earlier action,” said founder Dr Hsieh Chin-Hsing.

That distinction matters. AI-assisted consumer assessments can be useful for screening and education, but they also risk overpromising if positioned as medical diagnosis. The success of such systems depends not only on speed and convenience, but on clear communication about what a scan can and cannot tell a user, especially when the assessment leads straight to a product recommendation from the same company.

A competitive space for personalised foot care

GoodARCH is entering a market that already includes global and regional players using scanning, pressure mapping and customisation to sell insoles and footwear. Aetrex offers foot scanning technology through retail partners, while FootBalance provides customised insoles shaped around in-store analysis. Dr. Scholl’s has long used kiosk-based foot mapping in mass retail, and Superfeet has built a strong brand around performance and comfort insoles.

In Southeast Asia, competition also comes from podiatry clinics, physiotherapy centres, orthopaedic footwear providersand sports retailers offering gait analysis. This makes GoodARCH’s challenge twofold: it must convince consumers that its assessment is credible, while showing partners that its system can fit into existing wellness, rehabilitation or retail workflows.

Also Read: Why Southeast Asia’s next healthtech winners will be built around healthcare workflows, not just AI

The broader direction, however, is clear. Healthcare is moving beyond hospitals and clinics into pharmacies, gyms, shopping centres and homes. For healthtech startups in Southeast Asia, this shift opens space for tools that are faster, cheaper and easier to access than traditional specialist appointments.

GoodARCH’s Malaysian rollout is therefore less about a single foot scan than a wider question: how much preventive healthcare can be delivered before a person becomes a patient? If the company can answer that with enough clinical discipline and consumer trust, foot mapping may become part of a much larger wellness stack.

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A few years ago, the early stages of a video project often began almost from zero. We would talk through the client’s direction, work out a script, develop the visual approach and gradually turn a loose idea into something the client could react to.

That part has changed. Today, a client can give me a rough direction and I can use AI to produce a first script quickly, revise it and put something concrete back in front of them. From there, we can move into the rest of production with much less work spent creating everything from scratch.

The first draft became easier. The decision often did not.

A client may know what the video is for and still have difficulty describing what it should look or feel like.

Usually I start by asking whether there is a reference video or image close to what they have in mind. If there is, we can talk about something specific. If there is not, I will often find several examples myself and ask what feels right and what does not.

The conversation often becomes clearer at that point. One direction may feel too polished. Another looks too much like an advertisement. The pacing may work while the characters do not. Sometimes ruling something out tells us as much as choosing something.

Once the direction becomes clearer, the conversation also becomes more specific. We can move from “something like this” to decisions about the script, visual style, characters, scenes and eventually the storyboard. Each step narrows what the production side has to interpret on its own.

This is why I do not think of requirement discovery as a single conversation at the beginning of a project. In practice, it develops through a sequence of increasingly concrete choices. The earlier those choices become clear, the less likely the production team is to spend time building in the wrong direction.

Part of my job, then, is not simply to take an instruction and turn it into a video. It is to help the client arrive at the instruction in the first place.

AI helps me get something concrete in front of the client earlier. It does not remove the conversation that follows.

A different kind of uncertainty can appear later.

Also Read: SEA startup funding jumps to US$7.25B, but most founders are still waiting

I have worked on projects where the script was confirmed, the main visual direction was confirmed and the work had already reached a rough cut. From the production side, the major choices seemed settled. Then another person or department inside the client organisation reviewed the material, and earlier decisions reopened.

What looked like approval from one side of the project turned out to be one stage in a longer internal process. Only then do we discover that the decision was not actually closed.

Making another version may be straightforward. Knowing whether the people reviewing it are the people whose decision will hold can be harder.

A late change is not just another prompt or another edit. By that point, several parts of the work may already depend on the earlier decision. A changed script can affect images; changed images can affect video generation and editing; feedback then has to travel back through the people doing each part of the work.

Faster generation makes the replacement work easier. But the change still has to be passed back through the people doing the rest of the work. In some projects, that coordination takes more of my attention than producing the next asset itself.

This has changed where I spend my own time.

Once a requirement is clear, more of the execution can be handed to other people. Storyboarding, image generation, video generation and editing can all be handed over. I can set out how the work should be done and check it as it progresses.

That still leaves a lot of work with me: understanding what the client means, collecting feedback, passing that feedback back to the production side and checking that the next version still matches the agreed direction.

Also Read: SEA startup funding jumps to US$7.25B, but most founders are still waiting

I can hand off more of the making itself. The client conversations still come back to me.

A new version can be useful because it changes that conversation. The client becomes clearer about the direction. Two people who appeared to agree react differently to the same material. Someone new joins the review and reveals that an earlier approval was not final.

At other times, the pictures change, the script changes and the edit improves, but the same unresolved question remains behind the work.

That is the difference I now pay attention to. When the next version becomes easier to produce, I ask what changed because we made it. If the client is no clearer about what should be made and someone else still needs to approve it, we have produced faster without getting much closer to done.

We may simply have reached the same unresolved decision sooner.

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Hitting “send” on a bank transfer used to come with a cushion of time. A payment might sit in a queue for hours, sometimes days, giving banks and senders a window to notice a mistyped account number or a suspicious beneficiary. Instant payments have all but erased that cushion. Money now lands in seconds, and so do the mistakes.

Singapore-headquartered iPiD is building a business in that vanishing window. The payment intelligence company has raised US$16 million in a Series A round led by Foundation Capital, with Citi and HSBC joining as strategic investors.

Existing backers QED Investors, Monk’s Hill Ventures and Quona Capital also returned.

Also Read: From KYC to KYA: how AI agents are reshaping payment risk

The round will fund iPiD’s expansion in the US and Europe, the growth of its global verification network, and new products for US payment rails, stablecoins and other digital assets. It takes the company’s total disclosed funding to roughly US$24.6 million, following a US$3.3 million seed round in 2022 and a US$5.3 million pre-Series A in 2024.

Know your payee, not just your customer

Founded in 2021 by payments executives with SWIFT and fintech backgrounds, iPiD does one thing: it checks whether a recipient account is valid and whether its details match the intended beneficiary before money moves.

The company calls this “Know Your Payee”, a deliberate nod to know-your-customer rules. KYC tells a bank who is sending the money. iPiD wants to tell it where the money is going. The practical payoff is fewer failed payments, fewer misdirected transfers and, in some cases, a fraud attempt caught before the funds disappear.

The timing is hard to argue with. Authorities across the region are fighting a scam wave on multiple fronts, from Singapore tightening scam rules for messaging and e-commerce platforms to Thailand, where the scam epidemic is increasingly seen as a technology problem. Globally, deepfake fraud losses have hit US$3.7 billion. Faster rails make every one of those attacks quicker to execute and harder to reverse.

Why Southeast Asia makes the case

Southeast Asia is, in many ways, the perfect advertisement for iPiD’s problem. The region has sprinted ahead on instant domestic payments: Singapore’s PayNow, now moving into its second generation, Thailand’s PromptPay and Indonesia’s BI-FAST. Regulators are also stitching these systems together for cheaper regional transfers.

Yet the verification underneath remains stubbornly domestic. A PayNow user can see a recipient’s name before sending; a Singapore company paying a supplier in Jakarta, a gig worker in Manila or a creator in Mumbai often cannot rely on the same assurance once the transfer crosses a border. For businesses running multi-country payouts, one wrong digit can mean delayed settlement or an outright loss.

Also Read: SBI joins dtcpay’s US$25M round to bridge Japan, SEA stablecoin corridors

iPiD says its network now reaches financial institutions in more than 50 countries. Figures cited by Axios put its reach at more than 6,500 institutions and about four billion bank accounts, through direct connections and distribution partners. Those are company-supplied numbers, and the gap between “reach” and reliable, real-time coverage in every corridor is precisely where infrastructure businesses tend to be tested.

Banks as backers and buyers

The most telling detail in the round is not the amount but the names. Citi and HSBC are both investors and customers. iPiD says its technology sits inside Citi Verify, while HSBC uses it to extend beneficiary validation beyond local verification schemes. Visa, Nium, Experian and Tazapay are among its other partners.

That matters in a sector where growth cannot be bought with marketing budgets. Verification depends on access, trust and deep integration, and banks rarely hand sensitive account data to a provider they do not believe can handle it. Having two global banks on the cap table is, in effect, a due-diligence stamp.

It also creates a dependency worth watching. Partner landscapes shift quickly in payments: Tazapay, for one, is being acquired by Circle for US$400 million, while Nium has been pushing into stablecoins through its Cypher acquisition. Consolidation can open doors for a neutral verification layer, or close them.

The stablecoin bet

The newest and least proven part of iPiD’s plan is digital assets. Stablecoins, tokens pegged to fiat currencies such as the US dollar, are being explored for cross-border settlement because they move fast and around the clock. They are also unforgiving: send to the wrong wallet and the money may simply be gone.

For regulated firms, that is the payee question in new clothes. If stablecoins become routine for remittances or treasury operations, verification tools will need to cover wallets as well as bank accounts. But the market is young, its growth carries risks such as dollarisation that few are pricing in, and iPiD has not given a timetable for its digital-asset or US-rail products. Until customer deployments are announced, these remain ambitions.

Rivals on every rail

iPiD is far from alone. SWIFT offers Payment Pre-validation for cross-border transfers, SurePay and others provide confirmation-of-payee services in Europe, and the UK runs a national Confirmation of Payee framework. In the US, GIACT and Early Warning Services operate in adjacent account-verification and fraud-prevention segments.

Also Read: Meta, Singapore Police disrupt 3.7M scam-linked assets across Facebook and Instagram

In Southeast Asia, the competition is quieter but real: domestic instant-payment schemes and bank-led tools already validate names within their own markets. iPiD’s pitch is that nobody stitches these fragmented sources together across borders as well as it does. Proving that at scale, with integrations that do not break, will decide whether the premise holds. More checks do not automatically mean less fraud either, as the compliance paradox reminds us.

The round fits a broader shift in Singapore’s fintech scene, where investor money has migrated from wallets and consumer lending towards unglamorous infrastructure: orchestration, compliance, fraud and treasury. Verification is not flashy. But as money gets faster, knowing where it is going may become the part of the transaction nobody can afford to skip.

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Pipeline operators already receive large volumes of asset data, but physical activity along remote rights-of-way remains difficult to observe continuously. Vision AI is beginning to turn existing infrastructure into an additional layer of operational intelligence.

Midstream operators face a problem created by the infrastructure itself: pipelines can run for hundreds of kilometres through terrain that no one is watching most of the time.

In the United States alone, more than 2.6 million miles of oil and gas pipeline crisscross the country, much of it through rural, forested, or otherwise low-visibility terrain.  Since 2005, PHMSA has logged more than 875 excavation-related pipeline incidents in the US, resulting in 40 fatalities, 166 serious injuries, and roughly US$322 million in property damage.

That blind spot isn’t unique to any one country’s network and pipeline networks worldwide are only getting longer.

A growing network, a growing blind spot

The Middle East’s own pipeline footprint isn’t standing still either. According to the Organisation of Arab Petroleum Exporting Countries, the region’s operational oil and gas pipeline length grew eight per cent only in the year 2023, as national operators expand transmission networks to keep pace with export capacity and domestic demand. Saudi Arabia alone accounts for roughly 15 per cent of the region’s active pipeline length, spread across more than 80 individual lines, much of it crossing remote desert and coastal terrain with limited natural surveillance.

Operators have tried to solve this the same way for decades – aerial patrols, ground patrols by truck or on foot, and community awareness campaigns asking landowners and contractors to call before they dig. All three remain necessary. None of them are continuous.

The gap isn’t awareness as most operators run robust public-education and one-call programs, and contractors are frequently aware a line runs beneath them before they break ground. The gap is timing.

A patrol schedule, however well run, only tells you what happened at a corridor once every few days or weeks; it can’t tell you what’s happening right now, in the stretch between two scheduled passes, where an excavator or an unauthorized vehicle can do real damage in minutes.

Closing that gap requires shifting to continuous observation, which is where a newer layer of vision AI-based monitoring is starting to change the equation.

Turning a corridor into a monitored perimeter

The first layer closing that gap is what the industry calls area control — geo-fenced, camera-based monitoring that treats a pipeline right-of-way less like open land and more like a perimeter with a boundary that knows when it’s been crossed.

Also Read: SEA startup funding jumps to US$7.25B, but most founders are still waiting

Instead of a patrol discovering an intrusion after the fact, area control systems watch the corridor continuously and flag the moment a person, vehicle, or piece of heavy equipment enters the buffer zone, day or night, without needing a human to be looking at that exact stretch of camera feed at that exact moment. The alert reaches a control room the instant the geo-fence is breached, rather than whenever the next scheduled patrol happens to drive past, collapsing a response time that used to be measured in days or weeks down to seconds.

 

For a pipeline corridor specifically, that means the system isn’t just recording that an excavator showed up, it is distinguishing an excavator approaching the buffer zone from routine agricultural traffic passing nearby, and routing only the genuine breach to a human for a decision.

Extending coverage with mobile inspection

Camera towers and fixed sensors cover a lot of ground, but pipeline corridors routinely pass through terrain — floodplains, dense vegetation, mountainous stretches — where fixed infrastructure isn’t practical. That’s where vision AI-powered drone-based inspection has become the second layer of the system rather than a replacement for it.

Industry data on UAV-based pipeline monitoring shows the appeal. A peer-reviewed review of oil and gas drone-inspection research cites a North Sea operator survey finding that drone-based inspections can cut costs by roughly half and complete the same work around twenty times faster than conventional foot or vehicle patrols.

For operators managing corridors that stretch across deserts or offshore approach routes, that difference isn’t marginal, it’s the difference between inspecting a stretch of line once a month and inspecting it on a rolling, near-continuous basis.

Connecting visual events with operational context

None of this — cameras, geo-fences, drones — closes the loop on its own. Detection has existed in some form for years; the harder problem has always been turning thousands of hours of footage across a sprawling corridor network into something a control room can act on before damage occurs, not after.

That’s pushed the technology up a layer, from passive detection toward agentic AI intelligence that can reason across a live feed, correlating what a camera sees with what a drone just flagged, filtering out the wildlife and weather noise that would otherwise flood a control room with false positives, and surfacing only the encroachment risks that actually warrant a response.

Also Read: The future of healthcare AI isn’t more data. It’s better context

An Abu Dhabi-based oil and gas computer vision deployment saw 50 per cent improved annual productivity with 80 per cent reduction in violations, figures that depend as much on the reasoning layer filtering noise as on the cameras and drones doing the watching.

The economics of pipeline safety have always been distorted by distance. You can’t put a person on every kilometre of a corridor, and you shouldn’t have to. What’s changed is that these systems no longer just record what happened, cameras, drones, and the AI agents reasoning across them can now tell the difference between a routine crossing and a genuine threat, and do it before a shovel breaks ground. That’s the shift from surveillance to prevention.

What this means for midstream operators

None of this replaces the fundamentals of easements, signage, public awareness, and physical patrols. They remain part of any credible damage-prevention program. But the data on complacency-driven infringements suggests those measures alone have a ceiling, and a growing pipeline network only raises the stakes.

What’s changing is the layer sitting on top of the fundamentals: area control that turns a corridor into a monitored perimeter, drones that reach the stretches fixed cameras can’t, and an AI reasoning layer that decides what actually deserves a human’s attention. Individually, none of these are new technologies.

Combined and reasoning together, they close the one gap that decades of patrols never could, the time between when risk appears at the corridor and when someone finds out.

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There is a particular misery that visits finance departments in the last week of every month. The sales figure on the point-of-sale screen says one thing, the bank statement says another, and somebody has to explain the gap before the books close.

Gilang Gibranthama, co-founder of Jakarta-based Matchmade.io, has built a business around that misery. When the reconciliation platform turned its attention to Singapore, he expected a different problem altogether.

“It isn’t. That was the surprise,” he tells e27. Singapore, in his telling, has “the same failure wearing better clothes”: SGQR instead of Indonesia’s QRIS, PayNow and NETS in place of Indonesian e-wallets, but the same mismatch waiting at month-end.

Also Read: SEA’s SMEs aren’t lazy, but their payments infrastructure is

That observation is the crux of Matchmade.io’s regional pitch. The company reconciles data across banks, payment gateways, marketplaces, POS systems, ERP platforms and internal databases through a single platform, and counts retail, financial services and logistics names such as Bacha Coffee, Pizza Hut, CHAGEE and Wingstop among its clients. It already handles Singapore transactions for an unnamed multi-country operator, alongside that client’s other markets.

The unglamorous layer beneath the payments boom

Reconciliation, checking that every transaction in one system matches its counterpart in another, has rarely been a headline act. Southeast Asia’s fintech story has been told through wallets, QR codes and super apps instead. The region’s digital economy was projected to cross US$300 billion in GMV in 2025, according to the e-Conomy SEA 2025 report by Google, Temasek and Bain & Company, while Singapore’s own digital economy reached S$128.1 billion (nearly US$100 billion), or 18.6 per cent of GDP, in 2024.

But every new payment rail adds another settlement file, on its own schedule, with its own fees. Indonesia, where the digital payments race is entering a new phase, is a case in point. It is why some argue the next ASEAN fintech opportunity lies not in acceptance but in settlement intelligence.

Gilang’s more interesting argument is that marketers feel the pain before finance does. Promos, vouchers, loyalty redemptions and delivery aggregator commissions all eventually land as settlement lines. “If nobody can trace it, the campaign gets blamed for a gap it may not have caused,” he says. “Reconciliation is where marketing spend goes to be misunderstood.”

For consumer brands running dozens of outlets across several markets, a misattributed discrepancy can quietly kill a campaign that was actually working.

Why Indonesia was the training ground

Matchmade.io claims it can reconcile more than one million transaction records in under three minutes with up to 99 per cent accuracy, and cut the time needed to spot discrepancies by up to 99.5 per cent.

Gilang credits Indonesia for those numbers. The archipelago moves far more transaction volume than Singapore and is considerably more fragmented, he says, with every channel settling on its own terms. “There was no way to solve it halfway.” A system built there, he argues, arrives in Singapore “over-built for the problem instead of under-built”.

The commercial logic follows. Regional groups headquartered in the city-state typically reconcile market by market, with a different local team, spreadsheet and vendor in each. Consolidating onto one engine is cheaper, but Gilang says the bigger prize is consistency: the rules live in one place, so “adding the sixth market becomes a configuration change, not a project”.

Being Indonesian, he insists, has mattered far less than expected. “Nobody has ever asked us for our passport.” What Singapore buyers probe is accuracy, whether the vendor has seen their exact mix of channels before, and whether it can prove it. Singapore’s procurement and security reviews are heavier than most of the region’s, a hurdle he says opens the market properly once cleared.

Also Read: The end of manual finance? AI agents are coming for startup payments

His advice to fellow founders: “Sell the evidence, not the origin story.”

The AI pitch, and the fine print

Like nearly every B2B startup in 2026, Matchmade.io has an AI chapter. It pairs rules-based reconciliation with an AI-assisted interface and argues that clean data is the precondition for AI-driven forecasting and anomaly detection. “AI is only as reliable as the data behind it,” Gilang says.

The argument has merit. Much of enterprise AI in APAC remains stuck in the proof-of-concept room, often because the underlying data is a mess, and investors are noticing: enterprise infrastructure surged 503 per cent year-on-year in one recent tally. The next AI payments boom may well happen in the back office.

Still, the claims deserve scrutiny. “Up to 99 per cent” accuracy on a million records can still leave 10,000 unmatched entries, and in finance, the exceptions are where the work and the risk live. The company has not disclosed funding, revenue, customer numbers or independent validation of its benchmarks, and its Singapore reference client remains unnamed. Integration is its own minefield, too; cheap ERP implementations tend to carry hidden costs in this market.

A crowded ledger

Matchmade.io is far from alone. Global incumbents BlackLine, HighRadius and Trintech sell financial close and reconciliation software to large enterprises, while ERP vendors such as SAP and Oracle NetSuite bundle matching tools of their own.

In India, Cashfree acquired reconciliation startup Recko in 2021. Closer to home, payment players such as Xendit and PayMongo offer reconciliation tooling to merchants, so Matchmade.io must convince buyers that a neutral layer sitting across every provider beats the reports each gateway already hands them. Singapore’s evolving rails, including PayNow Gen 2, will only multiply the files that need matching.

Also Read: Profitable Qashier raises US$6M as SEA’s SME payments race intensifies

Its edge, if it holds, is neutrality plus depth earned in the region’s messiest market. Whether that wins over regional headquarters in Singapore will depend less on the pitch than on the evidence, which, fittingly, is exactly what Gilang says his buyers want.

The post Same failure, better clothes: Matchmade.io on selling Indonesian finance tech in Singapore appeared first on e27.

Asian stocks and bonds fell as a global bond selloff deepened and inflation fears intensified, with oil prices remaining elevated. Regional indices pointed lower in early trading. Wall Street had finished a volatile session mixed to flat. The S&P 500 edged slightly lower. The Dow Jones Industrial Average dropped 161 points, or 0.3 per cent. The Nasdaq composite gained less than 0.1 per cent, helped by names like Meta. Treasury yields climbed. The 10-year Treasury yield moved toward 5.2 per cent. The 30-year yield touched levels not seen since 2004. These moves reflected mounting rate-hike anxiety. The bond market became the centre of investor concern. Fixed income repriced across the globe. Borrowing costs rose. Risk assets faced immediate pressure. The traditional financial system showed how tightly connected its parts have become.

The bond selloff did not remain confined to one region. It spread through Asia and pressured regional equities. Investors watched inflation fears grow as oil prices stayed high. Energy costs feed into broader price pressures. Central banks then face difficult choices. They can raise rates to fight inflation. That path lifts yields further and hurts stocks. They can hold steady, risking inflation becoming entrenched. That path also unsettles bondholders. This tension explains why Asian stocks and bonds fell together. It also explains why Wall Street struggled for direction. The S&P 500 slipped. The Dow lost 161 points. The Nasdaq managed a tiny gain. Meta helped that index. The broader market still lacked a clear upward drive.

Treasury yields told the sharpest story. The 10-year yield climbed toward 5.2 per cent. The 30-year yield reached a level last seen in 2004. Those numbers matter because Treasury yields serve as a benchmark for mortgages, corporate loans, and equity valuations. When the long end of the curve moves this far, it signals that investors demand more compensation for holding government debt. It also signals concern about inflation over a longer horizon. Elevated oil prices feed that concern. Oil remains in focus across global trading desks. Every sustained rise in energy costs makes the fight against inflation harder. It also makes rate cuts less likely. That reality weighed on Asian markets and kept US investors cautious.

Also Read: Oil crashed 5% but Bitcoin jumped US$4K, altcoins surged 2X harder: What’s driving this?

The US session reflected this caution. Wall Street finished mixed to flat. The S&P 500 edged slightly lower. The Dow Jones Industrial Average dropped 161 points, or 0.3 per cent. The Nasdaq composite gained less than 0.1 per cent, helped by names like Meta. That narrow gain shows how selective the buying was. Investors weighed inflation and policy concerns. They did not abandon risk entirely. They simply favoured a few large names over the broad market. This pattern resembles the behaviour seen in other uncertain periods. Capital moves toward companies with clear earnings power or strong secular stories. It avoids the broader index until the macro picture becomes clear. The bond selloff made that clearing harder to see.

In the same 24-hour period, the crypto market rose 0.54 per cent to US$2.88 trillion. This move looks modest on its own. It becomes more interesting when set against the backdrop of the drop in Asian stocks and bonds. The crypto market showed a low correlation with traditional markets. It moved on crypto-specific developments. The primary reason was capital rotation into altcoins with strong institutional news. Real-world asset narratives led the way. Partnership announcements gave traders clear catalysts. Quant surged 26.66 per cent after announcing a partnership with The Clearing House for U.S. bank settlements. Ondo jumped 25.09 per cent following the launch of tokenised investment portfolios developed with BlackRock. These gains were not random. They reflected a deliberate pursuit of higher-beta assets tied to real-world utility and institutional adoption.

Secondary reasons supported this rotation. Bullish sentiment remained in place. The Fear and Greed Index stood at 73. That reading falls into Greed territory and supports a risk appetite. At the same time, leveraged risk fell. Total derivatives open interest dropped 11.7 per cent in 24 hours. Bitcoin liquidations fell 32 per cent. These figures point to an unwind of speculative positions. The rally therefore occurred alongside a reduction in systemic risk. That combination makes the move more structurally stable. It also makes a sharp forced reversal less likely. A market that rises while leverage falls is different from one that rises on borrowed conviction. The crypto session looked more like selective repositioning than a broad speculative frenzy.

Also Read: Fed cuts rates but warns against complacency: Bitcoin and altcoins react sharply

The near-term outlook for crypto depends on whether this altcoin rotation broadens or fizzles. If momentum holds, the market could test resistance near US$2.94 trillion. A break above that level could open a path toward US$3.03 trillion. Support sits at the 23.6 per cent Fibonacci retracement level near US$2.85 trillion. Failure to hold above US$2.85 trillion may signal a pause in the rotation. It would suggest that profit-taking is overwhelming rotational momentum. Traders will watch whether capital continues to flow into names with institutional catalysts. They will also watch whether Bitcoin attracts defensive flows if altcoin strength fades. The market’s next move depends on breadth. A narrow rotation can last for a while. It becomes fragile when only a few stories carry the entire advance.

The contrast between these two market environments is stark. Traditional assets faced synchronised pressure. Asian stocks and bonds fell. Regional indices pointed lower. The S&P 500 edged slightly lower. The Dow dropped 161 points. The Nasdaq gained less than 0.1 per cent. Treasury yields climbed toward 5.2 per cent on the 10-year. The 30-year yield touched levels not seen since 2004. Oil prices remained elevated. Inflation fears persisted. Crypto moved higher by 0.54 per cent to US$2.88 trillion. It drew strength from institutional partnerships, tokenisation news, and a leverage unwind. The two worlds responded to different forces. One reacted to central bank policy and energy costs. The other reacted to project-specific adoption and positioning.

This divergence does not mean crypto has escaped macro gravity. Rising yields can still drain liquidity from speculative assets over time. Higher borrowing costs can slow venture funding for crypto projects. A sustained bond selloff can eventually pull all risk assets lower. On this particular day, though, the immediate drivers differed. Traditional markets focused on inflation and rate-hike anxiety. Crypto focused on Real-World Assets and institutional partnerships. The data supports that split. Fear and Greed at 73 showed crypto traders were still willing to take risks. Open interest down 11.7 per cent and Bitcoin liquidations down 32 per cent showed that willingness did not rest on heavy leverage. The traditional side showed no such cushion. Bond yields rose. Equities struggled. Oil kept inflation fears alive.

The market outlook shows selective momentum. The crypto rise is not a broad-based surge. It is a focused rotation into altcoins with tangible catalysts. This pattern indicates a maturing market where fundamentals begin to differentiate performance. The key question for crypto is whether sector breadth expands to sustain the rally. The key question for traditional markets is whether bond yields and oil prices calm down. If they do not, pressure will continue. If they do, risk appetite may return. For now, the two markets march to different rhythms, and investors who notice that difference may find useful signals in the noise.

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The post Altcoins are running on real news, Wall Street is running on fear appeared first on e27.

San Francisco and New York still lead The Observer‘s inaugural AI Cities Index, but the more consequential finding sits further east. Six of the world’s top 10 AI cities are in East and Southeast Asia, and Seoul ranks first globally for implementation: whether a city has the institutions, systems and practitioners to put AI to work across business, government and public services.

Seoul’s edge rests on years of investment in digital infrastructure, a chip cluster anchored by Samsung Electronics and SK Hynix, and a city committee that helps SMEs adopt AI they could not fund alone. Singapore ranks fifth for implementation, proof that a small market can outperform when policy, talent and enterprise adoption pull in the same direction.

The index, which scores 57 cities across 36 countries, also notes that the performance gap between Chinese and US models has narrowed to 2.7%, from 31.6% in 2023.

For the region’s larger markets, the lesson is uncomfortable. Indonesia, Vietnam, Thailand, Malaysia and the Philippines do not need another Silicon Valley. They need city-level clusters with the talent, cloud capacity and patient capital to move AI beyond pilots. Singapore is the only one already in the top tier.

Read the full article here:

REGIONAL

SEA funding hits US$7.25B in H1, but DayOne alone took 62%: Strip out DayOne’s US$4.5B Series C and the region raised US$2.75B. Deal volume sank to its lowest since 2018, Indonesia managed just US$104M, and median Series A shrank to US$8M, per the Kickstart–DealStreetAsia report.

GCash parent Mynt lines up 20+ cornerstone investors ahead of IPO: BlackRock funds, IFC, Schroders and T. Rowe Price anchor the institutional tranche of what Reuters says could be a US$1.3B raise, the largest share sale in Philippine history. Pricing lands on 1 October.

Grab executives buy US$30M in stock amid share slump: Insiders signal confidence as the stock trades below recent highs. Senior Grab executives collectively purchased $30M in company shares, a move typically read as a bullish internal signal during periods of market weakness.

Kopi Kenangan founder raises personal stake via secondary share purchase: The founder’s acquisition of additional shares on the secondary market signals confidence in the coffee chain’s trajectory ahead of a potential liquidity event.

Animoca Brands pauses Currenc merger, delays IPO plans: The Hong Kong-based Web3 firm has put its merger with Currenc on hold, pushing back its IPO timeline amid shifting market conditions and strategic reassessment.

Bits Media lays off 21 staff after Gobi-backed restructuring: The Gobi-backed digital media firm cut 21 employees as part of a broader restructuring effort, reflecting continued pressure on ad-driven media businesses in SEA.

Nadiem Makarim’s prison sentence cut to 9 years on appeal: A Jakarta court reduced the Gojek co-founder’s sentence, a development with significant implications for Indonesia’s startup ecosystem and investor confidence.

Citi, HSBC back iPiD’s US$16M Series A to verify payees across borders: Foundation Capital led the round for the Singapore firm, whose tech already sits inside Citi Verify. It will fund US and Europe expansion and stablecoin products, though iPiD has given no timeline for its digital-asset tools.

Ex-Sea Malaysia CEO Howard Soh to lead Khazanah’s Jelawang Capital: Soh, who launched Lazada, Zalora and Foodpanda in Malaysia, joins from LemmaTree on 1 October. He inherits Malaysia’s national fund-of-funds, part of Khazanah’s MYR1B (US$245M) Dana Impak push to crowd private capital into local venture.

Alpha JWC ups Kopi Kenangan stake as revenue climbs 62%: Tybourne Capital and Horizons Ventures also raised their stakes in the ongoing round. The coffee chain’s Q2 EBITDA rose 86%, and it has opened 221 outlets this year, taking its footprint past 1,500 across seven markets.

3cat raises US$4M Series A to take used phones to the Philippines: The Malaysia-born, Singapore-incorporated retailer is betting that rising smartphone prices will push more Southeast Asian consumers, for whom the phone is both a work tool and a status symbol, towards pre-owned devices.

Malaysia’s EV sales double as local assembly replaces imports: Sales hit 47,508 units in January–August, up 103%, led by Proton’s e.MAS 5. But Kenanga expects a gradual shift, citing fuel subsidies and just 6,904 public charging bays against a 30,000 target for 2030.

NUS Enterprise rebrands as NUSX, adds Munich and Shanghai outposts: Its new AI engine, Nova, scores patents for licensing potential and ranks likely buyers, tackling a world where 70-80% of university patents never get licensed. NUSX wants one NUS venture earning US$100M a year by 2035.

A*STAR nearly doubles A*Start Central’s space for deeptech founders: The hub grows to 18,000 square feet, adding dedicated labs, metal 3D printing and an RF shielding room. Its 100-plus ventures have raised US$1.25B; about 30% came from outside A*STAR.

Pinterest and Shopee link creator shopping in Indonesia and Brazil: Eligible Shopee Affiliate creators can now connect their accounts to Pinterest and recommend products there, pulling Shopee closer to the moment shoppers first decide what they want, before they ever search a marketplace.

Life Lab Resources raises US$1M to turn food waste into fish feed: The Singapore startup treats food waste as feedstock for aquaculture feed, pairing one of the city-state’s persistent waste problems with the region’s rising demand for more sustainable fish-farming inputs.

Bonbon Mobility gets US$500K to put Vietnam’s car washes online: The Ho Chi Minh City startup is building a digital booking layer for a fragmented market where owners still arrange washes, detailing and maintenance through phone calls and word of mouth, with uneven standards.

GoodARCH brings AI foot scans to Malaysia with US$230K investment: The Taiwan-run arch-support brand is betting a five-minute scan can catch foot problems before they turn into knee or back pain, or before customers quietly start walking less.

SEA startups gain ground in Seedstars’s disability inclusion cohort: Visa Foundation backs the six-month SEED Inclusivity programme, whose third cohort of 15 ventures builds for people with disabilities, with companies linked to Indonesia, the Philippines, Singapore and Vietnam alongside India and Pakistan.

INTERVIEWS AND FEATURES

Matchmade.io on selling Indonesian fintech in Singapore: The founder reflects on pivoting from failure to build a cross-border fintech play, offering rare candour on the challenges of selling Indonesia-built financial technology to Singapore’s demanding market.

Cynthia Wihardja’s post puts a human face on TaniHub’s VC fallout: Her brother Donald, ex-MDI Ventures chief, is serving five years over the firm’s TaniHub investment. Her LinkedIn portrait of his prison routine sharpens the ecosystem’s hardest question: where failed VC bets end and crimes begin.

Why SEA’s expansion plans depend on undersea cables nobody sees: In late August, Viettel’s engineers shifted 800Gbps onto one cable, 300Gbps onto another and the rest over fibre through Laos. For startups scaling regionally, that hidden fragility belongs in the cost model.

What KoinWorks’ survival teaches Indonesia’s next SME lenders: The P2P boom that produced KoinWorks, Investree and Modalku has consolidated, and the number of OJK-licensed lenders has fallen sharply since 2022. The survivors now operate inside a far tighter regulatory frame.

Shein’s 70% valuation slide asks whether fast fashion has peaked: Valued at around US$100B in 2022, Shein listed in Hong Kong this month at roughly US$27B as its growth slowed. The piece asks whether the model’s cracks are cyclical or structural.

INTERNATIONAL

Binance invests US$100M in Circle, expands USDC ties: The deal deepens the relationship between the world’s largest crypto exchange and the USDC issuer, with potential ripple effects for stablecoin adoption across SEA’s crypto markets.

Moody’s says AI is splitting Asia-Pacific into a K-shaped economy: Singapore, Malaysia, Vietnam and Indonesia should outgrow China this year on AI-led exports and data-centre inflows, but households face weak demand and rate hikes. Moody’s warns the region is exposed if AI sentiment flips.

a16z opens a US$42M founders’ academy for high school graduates: The first year is tuition-free for about 50 students, but the Horowitz Andreessen Academy plans elite-university fees by 2028. Anthropic, OpenAI, Nvidia and Stripe are among ten corporate partners hoping to hire graduates.

US robotics firm Tacta Systems opens Singapore facility: The Singapore facility marks Tacta Systems’ first Asia-Pacific foothold, positioning the city-state as a regional hub for advanced robotics deployment and R&D.

SEMICONDUCTOR

Thailand approves US$80B chip strategy to build a full supply chain: The three-phase roadmap moves from assembly and testing into wafer fabrication by 2040, targets 230,000 jobs by 2050, and arrives days before Infineon opens its first Thai factory on 1 October.

Nexstrom raises US$12M to grow 2D chip materials on 12-inch wafers: Xora Innovation led the seed round for the Singapore startup, whose chief scientist once led TSMC’s post-silicon research. The hard part is making atomically thin materials such as MoS₂ uniform enough for production foundries.

Qualcomm’s new Snapdragons can run a 30B-parameter model on-device: The Snapdragon 8 Elite Gen 6 chips, built on TSMC’s 2nm process, target personal AI agents just as Counterpoint forecasts a 14% drop in smartphone shipments this year on soaring memory costs.

CYBERSECURITY

Meta, Singapore Police take down 3.7M scam-linked assets: The joint effort removed or restricted pages and accounts across Facebook and Instagram pushing fake skincare discounts and easy-money investment schemes, the ordinary-looking hooks through which many scams begin.

AI

Anthropic says Claude found a CRISPR-like enzyme system in 21 hours: About 950 agents burned 210M tokens to surface the system hidden in bacteriophage DNA. Humans still ran the lab experiments, and Dario Amodei concedes a Stanford team previously found something similar.

MAS stress test finds 32% of listed firms at risk in an AI crash: A 30% revenue shock and 400-basis-point rate spike would leave firms holding 16% of corporate debt vulnerable, the Financial Stability Review finds. Smaller, leveraged companies and lower-income HDB borrowers look most exposed.

Meta launches AI glasses in Singapore, eyes four more ASEAN markets: Prices start at S$349 (US$273). Indonesia, Thailand, Malaysia and the Philippines follow later this year, with Muse, Meta’s AI agent, set to handle bookings and other multi-step tasks hands-free.

70% of APAC consumers quit AI support chats that forget them: Twilio: Yet 84% of businesses believe their AI agents recognise returning customers. Only 22% of firms tell customers upfront that they are talking to a bot, though 70% of consumers want that disclosure.

Vision AI gives pipeline operators eyes on remote corridors: Pipelines run for hundreds of kilometres through terrain operators struggle to watch. Vision AI turns existing infrastructure into a continuous monitoring layer for physical activity along remote rights-of-way.

Grab and OpenAI launch AI training targeting 30,000 partners: The programme aims to upskill Grab’s merchant and driver partners across Southeast Asia, marking one of the region’s largest platform-led AI literacy initiatives to date.

Shrinking AI chips to power next-gen wearables: Advances in miniaturised AI chip design are enabling a new class of low-power wearables, with implications for consumer tech and health monitoring markets across SEA.

THOUGHT LEADERSHIP

Why Singapore, Indonesia and Vietnam may be losing the AI race: Singapore added S$1B (US$786M) for AI R&D, Indonesia wants 9M skilled digital workers by 2035, and Vietnam has elevated AI nationally. The author argues these headline commitments mask a race all three are quietly losing.

SEA’s digital investors may be diversifying in the wrong direction: App-based investing is curing home bias, but the author warns of a new concentration risk: millions of retail investors piling into the same handful of familiar global companies.

SME support in SEA needs connection, not more invention: Capital, training and compliance help for small businesses already exist. The author argues the gap is a system that links them around a single business journey, instead of leaving owners to assemble the pieces.

SEA’s next healthtech winners will fit clinical workflows first: An impressive AI model is not enough in Southeast Asian healthcare, the author argues. Startups that slot into how clinicians and hospitals already work stand a better chance than those selling standalone intelligence.

Asian investors no longer choose between crypto and TradFi: Gold shows the shift: Asian investors want the same assets as before, but new access routes strip out the brokerage accounts, fixed trading windows and siloed capital that traditional markets demand.

Asia’s ETF boom is outgrowing the infrastructure behind it: Asia is now the world’s fastest-growing ETF market, with assets, issuers and products multiplying. The question is whether the market plumbing underneath can keep pace with investor demand.

Containerisation shows why AI spending may continue without returns: BCG found 94% of organisations would keep investing in AI even without returns in 2026. The author borrows from shipping history to explain why infrastructure bets can outlast disappointing near-term payoffs.

AI will redesign how companies work, not just replace jobs: Drawing on enterprise deployments in Southeast Asia, the author argues the better question is how much more an organisation can accomplish once AI becomes part of how it operates.

AI won’t be your employee, but it changes what needs managing: The bigger shift is not faster drafting or summarising, the author argues, but a change in which work managers must oversee once AI takes on tasks inside everyday operations.

Your startup has an AI strategy. Where is its human strategy?: Fresh from LEAP in Saudi Arabia, the author notes founders obsess over what AI makes faster. The harder question is what people on the team should still own, and how to develop them.

Healthcare AI needs better context, not more data: Doctors never read every textbook before treating a patient; they weigh history, symptoms and patterns. The author argues clinical AI should work the same way, prioritising relevant context over sheer volume.

AI speeds up first drafts, not client decisions: A video producer finds clients now arrive with AI-generated scripts and visuals. The bottleneck has moved from producing a first draft to helping clients decide what they actually want.

How AI is reshaping precision farming: Remote sensing that flags nutrient gaps and plant stress is turning farming into a data-driven system, the author argues, one that promises tighter precision and higher yields.

Build vs buy: Why custom low-code tools are winning in 2026: Three in four organisations now embrace low-code. The author argues off-the-shelf software designed for every industry rarely fits specific needs, pushing more businesses to build their own tools.

Why scaling operations before data backfires for startups: Startups double headcount and add channels while still running on spreadsheets and gut-check meetings. The author explains how that data gap surfaces months later, when decisions start to break.

How one company escaped a culture of endless status meetings: Standups, syncs and stacked status calls eventually replace the work they were meant to support. The author explains how rebuilding the meeting culture began with asking which check-ins earned their place.

The founder’s dilemma: Why over-planning a startup backfires: Over an iced coffee in Kuala Lumpur, the author saw that chasing the perfect project tool, wiki and 18-month roadmap had shaped their startup the way it once shaped their over-planned trips.

What 16,625 publisher price lists reveal about backlink costs: ESBO Ltd, the author’s link-building agency, published its entire publisher database across 53 languages. The data shows founders what links really cost, and why the priciest placements are rarely just links.

Building an agency in Bangladesh, from the inside: Part 1: Ngital’s founder recalls client meetings where the buyer had already decided what they wanted within ten minutes, and what those early pitches taught the agency about selling in Bangladesh.

Why Ethereum keeps failing to break the US$2,800 wall: Even as Wall Street closed near record highs on 23 September and a tech rebound lifted Asian equities, Ethereum’s bulls stalled again at the same resistance level.

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