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.

The post Citi, HSBC back iPiD’s US$16M round to make instant payments safer across borders appeared first on e27.

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.

—

The post Vision AI expands visibility across remote pipeline corridors appeared first on e27.

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.

—

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 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.

The post Ecosystem Roundup: Seoul tops AI implementation as Singapore joins the global top tier appeared first on e27.

The digital asset market has fallen by 2.57 per cent to US$2.86T over the past 24 hours. Bitcoin, the largest token by market value, dropped 2.81 per cent to US$84,261.32 during the same period. The total crypto market cap declined 2.92 per cent, and Bitcoin’s move closely tracked that broader drop. The data shows a 96 per cent correlation with the S&P 500 and a 94 per cent correlation with Gold. Those numbers confirm that this move is not unique to crypto. Traditional markets and digital assets are responding to the same set of pressures.

Bitcoin’s drop triggered a leverage flush that cascaded into altcoins. Overbought conditions and a surge in derivatives open interest then amplified the pullback. The near-term outlook depends on whether Bitcoin holds above the US$2.76T market cap support, which sits near the 50 per cent Fibonacci level. A hold could open a rebound toward US$2.94T. A break below could extend losses toward US$2.65T.

The primary driver is macroeconomic. The Federal Reserve’s recent 25-basis-point rate hike and hawkish commentary fuelled concerns about further monetary tightening. At the same time, the 10-year US Treasury yield surged toward five per cent, its highest level since 2007. That move tightened financial conditions across the board. Strong US PMI data on September 23 reinforced expectations of persistent inflation and higher-for-longer rates.

As liquidity becomes less abundant, investors reduce exposure to risk-sensitive assets. Bitcoin behaved exactly like a risk asset in this environment. It sold off alongside traditional markets as participants priced in less liquidity. This macro backdrop matters because Bitcoin and other digital assets trade as long-duration risk assets.

When rates rise, the present value of future cash flows falls. Crypto does not have cash flows, but it still competes for capital. Higher yields make bonds more attractive. That shift reduces demand for speculative assets. The key items to watch are further statements from Fed officials and any movement in the 10-year yield. If that yield remains above 5 per cent, the pressure on risk assets could continue.

Also Read: Bitcoin’s US$87,000 spike: Real breakout or a US$900 million short squeeze?

A second force turned a measured decline into a violent flush. The initial macro-driven drop triggered a liquidation cascade. Data shows traders liquidated US$237 million in leveraged long positions in a single hour as Bitcoin broke below US$84,000. Over 24h, total Bitcoin long liquidations reached US$171 million.

Another measure shows US$158.95M in BTC long liquidations in 24h, a 243 per cent spike. Bitcoin dominance rose to 59.12 per cent as traders exited altcoin positions. This is a classic deleveraging event. Forced selling by overleveraged bulls accelerated the downward move, a typical sign of a crowded bullish trade unwinding. The scale of liquidations shows how crowded the long side had become.

A single hour produced US$237 million in long liquidations. The 24h total for Bitcoin longs reached US$171 million. The US$158.95M figure and 243 per cent spike confirm the same pattern. A stabilisation in funding rates and open interest would signal that the market has flushed out leverage. Until then, high liquidation volumes could point to further weakness.

The pain spread well beyond Bitcoin. Major altcoins underperformed the broader market. Avalanche fell 8.38 per cent, and Filecoin dropped 10.71 per cent. Both assets had enjoyed strong weekly rallies, with Avalanche up 36 per cent. That strength invited profit-taking.

The seven-day RSI for the total market hit an overbought 80.24. Traders rotated out of recently high-performing assets and into stablecoins or large caps. This rotation amplified the sell-off. Total open interest rose 11.13 per cent to US$493.14B even as prices fell. That combination indicates lingering leveraged positions that could fuel more volatility.

Avalanche and Filecoin had rallied hard. Avalanche gained 36 per cent in a week. That move left the market vulnerable. The 7-day RSI at 80.24 signalled overbought conditions. Profit-taking followed. Rotation into stablecoins or large caps is a defensive response. Sector rotation into stablecoins or large caps could continue if fear persists.

Also Read: Why did Bitcoin and Ethereum move in near-perfect lockstep after the Fed rate hike?

The near-term technical picture for Bitcoin now sits at a critical point. Bitcoin is testing the 23.6 per cent Fibonacci retracement level near US$84,432 after a rejection at the US$87,363 swing high. The structure remains corrective within a broader weekly uptrend of 10.56 per cent.

If Bitcoin holds above the US$84,000 support, it could retest US$87,000. A daily close below the US$82,000 to US$84,000 support band would shift focus toward the 38.2 per cent to 50 per cent Fibonacci retracement zone between US$79,600 and US$82,600. A deeper correction could reach the US$79,600-US$81,100 range. The US$84,432 level is the 23.6 per cent Fibonacci retracement. The rejection at the US$87,363 swing high set up the test.

The weekly uptrend remains positive at 10.56 per cent. A hold above US$84,000 keeps the US$87,000 retest in play. A close below US$82,000 to US$84,000 opens US$79,600 to US$82,600. The deeper zone is US$79,600 to US$81,100. The market will watch whether Bitcoin can absorb selling pressure and defend this zone.

The total crypto market cap faces a similar test. The key level is the 50 per cent Fibonacci retracement at US$2.76T. A hold above this support could lead to a rebound toward US$2.94T. A break below could extend losses toward US$2.65T. The pivot point sits at US$2.86T. Rising open interest alongside falling prices suggests that leveraged positions remain in the system.

The next 24h close relative to US$2.76T will matter. So will any shifts in spot ETF flow data. A rebound above the pivot at US$2.86T could target the recent high of US$2.94T. The 50 per cent Fibonacci at US$2.76T is the line. A rebound above the US$2.86T pivot could target US$2.94T. A break below US$2.76T could send the market to US$2.65T. Open interest at US$493.14B, up 11.13 per cent, shows leverage remains. ETF flow data is the next input.

My view is that this is a corrective pullback, not a reversal of Bitcoin’s strong weekly trend. The downturn has multiple drivers. Bitcoin liquidations started it. Altcoin profit-taking after a strong week worsened. The high correlation with traditional assets points to a macro-sensitive environment. Bitcoin and the broader crypto market remain connected to global interest rates and liquidity cycles.

For now, the evidence favours a liquidity-driven pullback, amplified by excessive leverage, rather than a change in the longer-term trend. Let’s see.

—

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 Bitcoin’s corrective pullback or the start of a deeper drop toward US$79,600? appeared first on e27.

Singapore’s food waste problem is often framed as a consumer habit or a logistics issue. For Life Lab Resources, it is also a feedstock problem, and one that could help ease another pressure point in Southeast Asia: rising demand for more sustainable aquaculture feed.

The Singapore-based startup has raised US$1 million in fresh funding to expand its capacity to process treated food substrates and produce nutrient-rich feed for fish and other aquaculture species.

The round was led by Decarb123, which invested through DC123FW. Keng Eng Kee Holding also participated, alongside follow-on backing from AC S323, an angel syndicate led by Huang Shao-Ning.

Also Read: Winnow buys Lumitics as hotel kitchens turn to AI to cut food waste

The financing is modest by venture capital standards, but the signal is larger than the cheque size. It points to growing investor interest in circular-economy startups that can turn waste streams into industrial inputs, particularly in markets such as Singapore, where land, food security, and waste management are tightly linked.

For Life Lab Resources, the funding is meant to increase production capacity rather than finance a speculative bet. The company said the capital will allow it to process more treated food substrates and supply more aquaculture feed to farmers. It also plans to develop more circular food products, though the near-term focus appears to be on feed.

Why feed matters

Aquaculture is one of Southeast Asia’s most important food sectors. The region produces a large share of the world’s farmed fish and shrimp, and demand continues to grow as incomes rise and consumers look for affordable protein. But the industry has a feed problem.

Conventional aquaculture feed often depends on fishmeal and fish oil, ingredients made from wild-caught fish. These inputs are nutritious and widely used, but they are exposed to price volatility, supply constraints, and environmental concerns. Soy and other plant-based ingredients are common alternatives, but they come with their own land-use and nutrition trade-offs.

That is why startups and researchers across the region are experimenting with new inputs, from insect protein and microbial ingredients to agricultural by-products and upcycled food waste. The goal is not simply to make feed cheaper. It is to reduce dependence on stressed supply chains while maintaining the nutritional quality farmers need to raise healthy stock.

Life Lab Resources sits within this broader shift. By converting food waste into usable feed ingredients, the company is trying to solve two problems at once: diverting waste from disposal and creating a more circular input for aquaculture.

The model is especially relevant in Singapore. The city-state imports more than 90 per cent of its food and has made food resilience a policy priority. At the same time, food waste remains one of its major waste streams. Turning that waste into feed is not a silver bullet, but it fits neatly into Singapore’s push to extract more value from resources that would otherwise be discarded.

The circular-economy bet

Circular-economy startups often sound compelling on paper but can be difficult to scale. Waste streams are inconsistent. Processing costs can be high. Customers in traditional sectors such as agriculture and aquaculture are price-sensitive. A feed ingredient that works in a lab still has to perform reliably on farms, meet safety rules, and compete with established suppliers.

That makes capacity expansion an important milestone. If Life Lab Resources can process larger volumes of treated food substrates, it has a better chance of proving that its model can work beyond pilot scale. For aquaculture farmers, reliability matters as much as sustainability: feed has to be available, safe, nutritionally consistent, and sensibly priced.

Also Read: DELOS sparks ‘Blue Revolution’ in Indonesian aquaculture with Series A led by Monk’s Hill Ventures

The involvement of investors such as Decarb123 suggests an appetite for businesses at the intersection of climate, waste reduction, and food systems. Keng Eng Kee Holding’s participation is also notable because it ties the round to Singapore’s food and beverage sector, where waste is generated daily and circular models could eventually become part of operating practice.

Follow-on investment from AC S323 adds another layer of continuity. In early-stage climate and foodtech, repeat backers often matter because technical validation and commercial adoption can take longer than in pure software businesses.

Regulation will shape the pace

The biggest constraint may not be demand, but regulation. In Singapore, companies that manufacture feed for food-producing animals require a licence, and the Singapore Food Agency sets rules for some alternative feed inputs, particularly waste-derived materials.

These rules exist for good reason. Feed safety is directly linked to food safety. Inputs must be managed carefully to avoid contamination, disease risks, or harmful residues entering the food chain. For a company working with treated food substrates, compliance is not a side issue; it is central to whether the business can scale.

That regulatory burden can slow young companies down, but it can also become a barrier to entry once standards are met. In a sector where trust is critical, licensed and compliant operators may have an edge over informal or poorly controlled waste-to-feed models.

Singapore’s stricter environment could also become a proving ground. A startup that can meet the city-state’s safety requirements and demonstrate commercial viability may be better positioned to work with partners elsewhere in Southeast Asia, where aquaculture production is much larger but regulatory systems vary widely.

A crowded field, but not a settled one

Life Lab Resources is not alone in chasing the alternative feed opportunity. Across the region, companies such as Nutrition Technologies, Entobel, Protenga, and Inseact have built businesses around insect-based protein and other upcycled ingredients for animal and aquaculture feed. Globally, firms including Ÿnsect and Innovafeed have attracted significant capital to produce insect protein at industrial scale.

These are not like-for-like competitors. Some focus on black soldier fly larvae, others on specific agricultural by-products, while Life Lab Resources’s approach centres on treated food substrates. But they are all competing for a place in the same changing feed supply chain, one where farmers, feed mills, and regulators are testing whether alternative inputs can match conventional ingredients on nutrition, safety, cost, and scale.

For Life Lab Resources, that means the opportunity is real but execution will be unforgiving. The company must show that its feed performs consistently, that its supply of waste-derived substrate can be managed at volume, and that its economics work without leaning on sustainability claims alone.

Southeast Asia’s food systems are looking for practical fixes

The timing is favourable. Governments and companies across Southeast Asia are looking for ways to reduce food waste, improve food security, and lower the environmental impact of agriculture. Aquaculture is central to that discussion because it is both a major source of protein and a resource-intensive industry.

Yet the most successful solutions are likely to be practical rather than ideological. Farmers will adopt alternative feeds if they help maintain yields, protect animal health, and make economic sense. Food businesses will join circular waste models if collection, treatment, and compliance are manageable. Investors will stay interested if startups can move from promising pilots to repeatable production.

Also Read: Arus Oil is powering Malaysia’s circular economy by transforming used cooking oil into clean energy

Life Lab Resources’ US$1 million round does not answer all those questions. What it does show is that capital is still available for focused, infrastructure-heavy climate and foodtech companies when the problem is clear and the pathway to revenue is visible.

In a region where food demand is rising and waste remains stubbornly high, the idea of turning leftovers into feed is easy to understand. The hard part is building the system around it. That is the work Life Lab Resources now has more room to pursue.

The post Life Lab Resources grabs US$1M to turn food waste into aquaculture feed appeared first on e27.

Southeast Asia’s startup funding market is showing signs of life again, but the recovery is not reaching most founders.

Venture-backed companies in the region raised US$7.25 billion across 217 equity deals in the first half of 2026, according to the Southeast Asia Startup Funding Report H1 2026 by Kickstart Ventures and DealStreetAsia. That is the strongest half-year total since H1 2022 and nearly four times the US$1.86 billion recorded a year earlier.

On the surface, it looks like a sharp rebound after two difficult years. Look closer, however, and the picture is far more uneven. Deal volume fell 5 per cent year on year and is now nearly 62 per cent below the H1 2022 peak of 572 transactions. The number of equity deals is the lowest the report has recorded since 2018.

Also Read: Late-stage deals revive in Southeast Asia, but early-stage founders remain under pressure

The message is clear: capital is back, but it is not back for everyone.

A recovery led by a few large cheques

The rebound was heavily concentrated among a small group of companies. The five largest rounds accounted for 75.5 per cent of all equity funding, while the top 20 took 89 per cent. The remaining 196 transactions shared just US$800 million.

Much of this reflects a global shift in investor appetite. Capital is flowing into AI, compute infrastructure, semiconductors, robotics, defence technology and other areas seen as strategically important. KPMG recorded US$227.4 billion in global venture investment in Q2 2026, the second-highest quarterly total on record, with AI companies driving many of the largest financings.

Southeast Asia is participating in that cycle, but mainly through infrastructure and enabling technologies rather than a broad-based revival across consumer internet, fintech or software.

The clearest example is DayOne, the data-centre operator whose US$4.5 billion Series C round accounted for 62 per cent of all equity funding in the region during the period. The deal is tied to the global buildout of AI and cloud-computing capacity, where investors are backing the physical infrastructure required to train and run large AI models.

Without DayOne’s round, Southeast Asia’s equity funding would have stood at US$2.75 billion. That would still be 80.1 per cent higher than H1 2025, but far less dramatic than the headline figure suggests.

Minette Navarrete, President and Managing Partner at Kickstart Ventures, said the region is benefiting from investors seeking stability, diversification and supply-chain resilience. But she warned against mistaking capital concentration for market strength.

“Deal volume remains a better measure of market momentum, and on that measure, capital has returned but not broadly, and that tells us Southeast Asia’s recovery is still finding its footing,” she said.

Singapore pulls further ahead

Singapore once again dominated the region’s funding landscape. Companies headquartered in the city-state raised US$6.7 billion across 145 deals, representing 92 per cent of disclosed equity funding and 67 per cent of deal volume.

That lead partly reflects Singapore’s role as Southeast Asia’s financing hub. Many companies based there raise money to support regional or global expansion, not just domestic activity. Even so, the gap between Singapore and the rest of the region has widened sharply.

Indonesia, Southeast Asia’s largest digital economy by population and internet users, recorded only 17 deals and US$104 million in funding. No Indonesian company appeared among the region’s top 20 equity rounds. That points to an acute shortage of growth capital in a market that, only a few years ago, was producing some of the region’s biggest venture-backed names.

Malaysia showed the strongest breadth outside Singapore, with 27 deals worth US$203 million. Vietnam raised US$340 million across ten transactions, making it the second-largest market by value, although Vinpearl’s US$255 million round made up three-quarters of that total.

Also Read: When debt replaces equity: How SEA startups mask a funding winter

Thailand and the Philippines showed a similar dependence on single large deals. Amity Solutions accounted for about 77 per cent of Thailand’s US$130 million, while Salmon represented 75 per cent of the Philippines’ US$80 million.

Andi Haswidi, Head of Research at DealStreetAsia, said the US$7.25 billion headline should not distract from the weaker base underneath.

“Every market outside Singapore depended on one or two transactions to make its total. That is characteristic of what a market without depth looks like in any conditions,” he said.

The early-stage warning sign

The most worrying signal is at the early stage, where the next generation of growth companies is supposed to form.

Early-stage deal count fell to 195 in H1 2026, down 11 per cent year on year, 63 per cent below the H1 2022 peak and well short of the 353 deals recorded in H1 2024. If fewer startups are being funded today, the region could face a thinner pipeline of Series A, growth-stage and exit candidates in the years ahead.

Yet the amount of capital going into early-stage companies rose. Startups at this level raised US$1.72 billion, up 56.4 per cent year on year. Median pre-seed funding reached a series high of US$900,000, while median seed funding rose to US$3.7 million.

Investors, in other words, are writing larger cheques for a smaller group of companies. Founders who clear the bar may get more runway. Those outside investors’ preferred sectors or networks may find the door harder to open.

Series A remains the bottleneck. The median Series A round fell from US$11.6 million in H2 2025 to US$8 million, while the average dropped from US$17.6 million to US$12.9 million. Bigger seed rounds, therefore, do not necessarily mean more startups are graduating to institutional Series A financing.

Investors may instead be giving selected companies more time to prove product-market fit before facing a tougher priced round.

A broader but tougher capital stack

Another shift is the changing mix of capital providers. Sovereign funds, corporates, private equity investors and private credit providers are playing a larger role alongside traditional venture capital.

That can give founders access to deeper pools of money and strategic industry relationships. But these investors often assess risk differently from venture funds. They may care more about commercial traction, infrastructure value, strategic relevance or predictable cash flows than about high-growth narratives alone.

Debt is also becoming more common. Debt financing rose to 23 transactions from 17 a year earlier, with total value reaching US$1.36 billion. The implied average debt deal fell to US$59 million from about US$99 million in H1 2025, suggesting debt is being used by a wider set of companies rather than only a few large borrowers.

For Southeast Asian startups, this is both an opportunity and a constraint. The funding market is no longer only about persuading venture capitalists to bet on growth. Founders may need to assemble different types of capital for different stages of expansion.

Still waiting for a broad recovery

The macro backdrop is not weak. Developing Southeast Asia is expected to grow by about 4.6 per cent in 2026, while data-centre investment and technology exports are supporting markets such as Malaysia, Thailand and Vietnam.

But startup funding remains highly exposed to global conditions, including US interest rates, exit markets, currency risk and liquidity among limited partners. Until exits improve and more capital returns to regional venture funds, fundraising is likely to remain selective.

Also Read: 48 PE investors, US$3.96B deployed, and not a single IPO exit in five years. Something is broken.

The first half of 2026 shows that Southeast Asia can still attract large pools of capital when companies sit at the intersection of AI, infrastructure and global strategic demand. What it has not yet shown is a full recovery for the wider startup ecosystem.

For most founders, the funding winter has not ended. It has simply become more selective.

The post SEA startup funding jumps to US$7.25B, but most founders are still waiting appeared first on e27.

Indonesia’s SME lending ecosystem looks different in 2026 than it did five years ago. The peer-to-peer lending sector that produced KoinWorks, Investree, Modalku, Akseleran, and dozens of others has consolidated. The number of OJK-licensed P2P lenders has fallen sharply since 2022. The platforms that remain — KoinWorks among the most prominent — operate inside a tighter regulatory perimeter, with a more institutional funding mix, and with a credit posture that resembles traditional non-bank lending more than the original P2P model.

After fifteen years inside Indonesian risk functions, I have watched this transition with more interest than most parts of the industry. The SME credit gap the original P2P model was designed to address is still real — arguably larger now than it was in 2018.

What the P2P era built

The Indonesian P2P sector that emerged after OJK’s POJK 77/2016 did something the traditional banking system had not. It built credit access infrastructure for SMEs that conventional banks were not set up to serve — too small for commercial banking, too formal for microfinance, too thinly documented for traditional underwriting.

KoinWorks, founded by Benedicto Haryono and Willy Arifin in 2016, was one of the platforms that built deliberately for that segment from early on. Its emphasis on productive SME credit, on developing alternative data underwriting capability, and on maintaining a measured growth trajectory gave it a more durable position than many peers when the sector consolidated.

What changed

Three structural shifts have reshaped the sector.

Funding mix institutionalised. The original retail-investor-to-SME model has been steadily replaced by institutional funding — banks, asset managers, structured-credit vehicles. The platform’s role shifted from retail marketplace to credit origination intermediary.

Also Read: What I’m learning about the second wave of insurance digital transformation in Indonesia

Regulatory perimeter tightened. POJK 10/2022 and subsequent rules raised capital requirements, codified credit risk management expectations, and required clearer governance. Smaller platforms could not absorb the compliance cost. Consolidation followed.

Credit posture matured. The early sector underestimated default risk in the segments it served, partly because alternative data models were younger than the underwriting confidence they produced. Surviving platforms rebuilt credit policy around tighter limits, more conservative scoring, and active portfolio management.

Lessons learned

Six principles from this decade are worth carrying into the next chapter of Indonesian SME credit.

Discipline beats velocity. The platforms that survived grew slower than the market wanted them to. The ones that did not are mostly no longer licensed. Underwriting discipline is not a brake on growth — it is the condition for it.

Funding mix is survival, not treasury. A platform with multiple institutional funding lines has options. A platform with one funding line of any kind has a deadline.

Regulator engagement compounds. The platforms that spent time with OJK before the rules tightened got more flexibility when they tightened. Time-with-supervisors is the most under-priced asset in fintech.

Alternative data is a hypothesis, not a verdict. Models built on novel data require longer back-testing than founder optimism typically permits. Models should be continuously revisable, never declared proven.

Also Read: A 90-episode series in 3 weeks: How AI is speeding up Indonesia’s creative economy

The credit gap is durable, the model is not. Indonesia’s SME credit gap will exist as long as the banking system is structured the way it is. Founders who anchor on the gap rather than on the specific model will adapt faster.

Consolidation cycles repeat. The 2022-2024 P2P shake-out was not a one-off. The next category — embedded finance, vertical lenders, supply-chain credit — will go through its own version of this cycle inside five to seven years. The platforms that prepare for it instead of treating this round as the last one will still be operating after the next.

The macro stakes

KoinWorks and the platforms that came up with it gave Indonesia’s SME segment an underwriting infrastructure designed for them rather than adapted reluctantly from larger products. That contribution has not been fully absorbed by the formal banking system, and the gap remains for the next generation to address.

The lessons from the P2P era — discipline, diversification, alignment, humility about data, durability of the problem — are the foundation. The opportunity to build on them is still open. The institutions that build well in the next five years will be the ones that treat the previous five years as research, not as critique.

—

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 KoinWorks era: What Indonesia’s SME lending journey has taught the next generation appeared first on e27.

The global race to lead artificial intelligence is usually framed as a contest between countries: the US versus China, with Europe, India and a handful of others trying to carve out space. A new ranking argues that this view misses where much of the action is actually happening.

The Observer’s inaugural AI Cities Index, released today, maps AI capacity at the metropolitan level, scoring 57 cities across 36 countries. The report looks at three broad areas: investment, innovation and implementation. Put simply, it asks which cities are attracting money, producing breakthroughs, and building the institutions and infrastructure needed to use AI in the real world.

Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning

The answer still begins in the US. San Francisco and New York take the top two positions overall, underlining America’s continued strength in frontier AI research, venture capital and commercialisation. But the more striking finding is how quickly East and Southeast Asian cities are closing the gap.

Six of the world’s top 10 AI cities are in East and Southeast Asia: Seoul, Tokyo, Singapore, Shanghai, Shenzhen and Beijing. Together, they point to a shift in AI power away from a few US technology clusters and towards a more distributed network of Asian hubs, many of which lean on state coordination, industrial depth and public-sector adoption rather than venture funding alone.

The city as the new AI battleground

The Observer says the index builds on the Global AI Index, which has benchmarked national AI performance for seven years. Its city-level approach matters because AI ecosystems are rarely spread evenly across a country.

The US may be the world’s most influential AI market, but much of its cutting-edge activity is concentrated in and around San Francisco. China, by contrast, has three cities in the global top 10: Beijing, Shenzhen and Shanghai. South Korea’s AI strength is heavily concentrated in Seoul, while Singapore functions as both city and national AI platform.

This is particularly relevant for Southeast Asia, where AI adoption is unlikely to be driven by national scale alone. Singapore’s fifth-place ranking for implementation shows how a smaller market can punch above its weight when policy, talent, digital public infrastructure and enterprise adoption move in the same direction. For neighbouring markets such as Indonesia, Malaysia, Thailand, Vietnam and the Philippines, the question is not whether they can replicate Silicon Valley. It is whether they can build city-level clusters with enough talent, cloud capacity, industry demand and policy support to make AI useful beyond pilot projects.

Also Read: Korea’s startup ecosystem is training founders, not just funding them

The index defines implementation as the presence of institutions, systems and practitioners needed to operationalise AI across business, government, education and communities. That distinction is important. Building a powerful model is one thing. Putting AI into factories, hospitals, classrooms, banks and public services is another.

Seoul’s implementation edge

Seoul emerges as the most prominent non-US city in the ranking. While San Francisco tops the overall index, the South Korean capital ranks first globally for AI implementation.

That result reflects South Korea’s long-running investment in digital infrastructure, advanced manufacturing and semiconductors. Seoul is home to a dense cluster of semiconductor headquarters and related technology firms, including Samsung Electronics and SK Hynix, two of the world’s most important memory chipmakers. Both sit at the centre of the AI hardware boom, as demand for high-bandwidth memory and advanced chips rises with the growth of generative AI.

The city has also created the Seoul AI Innovation Committee to support small and medium-sized enterprises that cannot easily fund in-house AI talent or infrastructure. This is the kind of policy plumbing that rarely attracts the same attention as a new chatbot or chip launch, but it may prove more important in determining which economies actually benefit from AI.

For founders and operators in Southeast Asia, Seoul’s example is instructive. Many businesses in the region are not trying to train frontier models. They are trying to automate customer service, improve logistics, detect fraud, optimise energy use or equip workers with better tools. The winners may be cities that help ordinary companies adopt AI safely and affordably, not just those that host the biggest research labs.

Tokyo ranks third and Singapore fifth in the implementation table, while Shanghai, Shenzhen and Beijing also appear in the top 10. The report contrasts these cities with San Francisco and New York, where adoption is described as more fragmented and often still at pilot scale, despite deep private-sector innovation.

US still leads in capital and breakthroughs

None of this means the US is losing its AI lead. San Francisco remains the world’s strongest AI city overall, ranking highly across innovation, investment and implementation. New York also scores well across the board, helped by its deep capital markets, enterprise customer base, universities and growing AI startup scene.

On innovation, the report describes a more direct contest between Chinese cities and US technology hubs. San Francisco ranks first, followed by Beijing, New York and Shenzhen. This mirrors broader industry trends: the US continues to produce more frontier AI models, but Chinese labs and companies have narrowed the performance gap.

The Observer cites wider research showing that the performance gap between Chinese and American AI models has fallen to 2.7 per cent, down from as much as 31.6 per cent in 2023. The US still produced more frontier models in 2025, with 50 compared with China’s 30, but China’s count doubled year on year.

That narrowing gap matters for Asia’s startup ecosystem. If high-performing AI models become cheaper, more open and more widely available, the advantage may shift from those who own the models to those who know how to apply them in specific markets. Southeast Asian startups, often built around fragmented languages, regulations and customer behaviours, could benefit from this shift if they can localise AI effectively.

Europe struggles for space

Europe’s showing is comparatively modest. Only London and Paris make the global top 10. That reflects a familiar challenge: Europe has strong universities, research talent and regulatory influence, but has struggled to match the US in venture-backed scaling or East Asia in coordinated industrial deployment.

The ranking also suggests that state support alone does not guarantee implementation strength. Cities with prominent technology ambitions, including Tel Aviv and Dubai, do not make the top 10 for AI implementation.

Patricia Clarke, The Observer’s technology editor, said the index shows that AI power is being redrawn around cities rather than countries. “Some of tomorrow’s most important AI decisions may not be made in Washington or Beijing, but in Seoul, Shenzhen and a handful of other emerging hubs,” she said.

Also Read: A Southeast Asia AI adoption outlook vs alternative global hubs

For Southeast Asia, that is both a challenge and an opening. Singapore is already in the top tier for implementation, but the region’s larger markets still need deeper AI talent pools, stronger cloud and data infrastructure, clearer rules and more patient capital for applied AI. The cities that get those basics right may not dominate headlines like San Francisco, but they could determine how AI changes everyday business across the region.

The AI race is still led by the US. But if The Observer’s index is any guide, the next phase will be fought less by countries in the abstract and more by cities that can turn AI from promise into infrastructure.

The post Seoul tops global AI implementation as Singapore enters top tier appeared first on e27.