Everyone wants to build the pipes. OpenAI, Stripe, Google, Visa — the race to let AI agents spend money autonomously is well funded and well covered. What is conspicuously absent is the layer that asks a far more uncomfortable question: should the agent be spending that money at all, and can the data driving that decision actually be trusted?

Telegraph Protocol’s Mark Basa and Ahmed Ali are not wrong to raise this. Hallucinations in a chatbot are a UX annoyance. Hallucinations in a system authorised to execute financial transactions are a liability event. And when those errors happen at machine speed, across multiple merchants simultaneously, the damage compounds before any human can intervene.

The liability question is equally unresolved. Existing legal frameworks assume human intent somewhere in the chain. Autonomous agents break that assumption entirely, and no regulator is close to filling the gap.

The fragmentation of competing commerce protocols — AP2, Stripe’s ACP, Shopify’s UCP — makes the problem worse, not better. A neutral verification layer sounds like an obvious solution. The harder question is whether the industry will prioritise it before the first systemic failure forces the issue.

REGIONAL

Indonesia plans to embed AI in its US$1.5B free meal programme: The government intends to integrate AI across key public programmes, including its flagship nutrition initiative, signalling a shift toward AI-driven public service delivery at scale.

Sea Limited and OpenAI to expand AI access on Shopee: The partnership will bring OpenAI’s capabilities to Shopee’s users and sellers across Southeast Asia, marking one of the region’s most significant e-commerce AI tie-ups to date.

Indonesia orders Shopee, TikTok Shop, Lazada to cut fees: Jakarta has directed the region’s biggest e-commerce platforms to reduce seller fees, a regulatory move that could reshape margins and competitive dynamics across Southeast Asia’s largest market.

MDEC names Ganesh Kumar Bangah as non-executive chairman: Malaysia’s digital economy agency has appointed the industry veteran to lead its board, a significant governance move as Malaysia accelerates its push to become the region’s digital hub.

Singapore AI inspection startup H3 Zoom raises US$3.6M: The funding will support expansion of H3 Zoom’s AI-powered visual inspection technology, which targets manufacturing and infrastructure sectors across the region.

ChemT nets US$4M to ease cell therapy manufacturing: Singapore-based ChemT raised US$4M to simplify the production of cell therapies, addressing a critical bottleneck in the commercialisation of next-generation medical treatments.

NewGen doubles down on K25 AI livestreaming platform: The company is pushing ahead with a commercial launch of its Asia-focused AI livestreaming platform, targeting the region’s fast-growing creator economy and live commerce market.

FileAI closes funding round to scale document intelligence: Singapore-based FileAI secured fresh capital to expand its AI-powered document processing platform, which automates back-office workflows for enterprises across Southeast Asia.

WeRide partners with Geely to bring robotaxi to Hong Kong: The deal marks a significant step for autonomous mobility in the region, combining WeRide’s self-driving software with Geely’s vehicle manufacturing scale.

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INTERVIEWS & FEATURES

Agentic commerce’s dirty secret: product data is often wrong: The core problem undermining AI-driven purchasing agents is inaccurate or incomplete product data, which causes errors at scale when AI makes buying decisions autonomously.

15 Thai AI companies betting on products, not hype: A roundup of Thailand’s emerging AI builders reveals a growing cohort of startups focused on domain-specific products in healthcare, logistics, and finance rather than foundational model development.

BioArk: building Asia’s life sciences infrastructure: An in-depth profile of BioArk’s strategy to become the backbone of biotech and life sciences manufacturing and logistics across the Asia-Pacific region.

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INTERNATIONAL

Groq confirms US$650M raise after Nvidia’s US$20B non-deal: AI chipmaker Groq confirmed the fundraise and said it is re-staffing after Nvidia’s reported acqui-hire attempt collapsed, underscoring the fierce competition for AI inference infrastructure.

Inside Zepto’s profit push ahead of IPO: The Indian quick-commerce firm is aggressively restructuring its unit economics to prove profitability before listing, a playbook that carries clear lessons for SEA’s own quick-commerce players.

Meta taps CRED founder Kunal Shah for WhatsApp, invests US$900M: Meta has appointed India’s Kunal Shah as WhatsApp’s new chief and poured US$900M into CRED, a dual move that deepens Meta’s strategic bet on the South and Southeast Asian market.

Nobel laureate John Jumper leaves DeepMind for Anthropic: The departure of the AlphaFold architect signals an intensifying talent war among frontier AI labs, with direct implications for biotech and AI research investment across Asia.

Trump crackdown on Anthropic: who benefits?: An analysis of how US regulatory pressure on Anthropic could accelerate the rise of rival AI labs and open doors for non-US AI providers in markets like Southeast Asia.

Tech layoffs in 2026: AI cited as leading cause: A running tracker of major global tech layoffs this year shows AI automation as the dominant rationale, a trend with growing workforce implications for SEA’s tech sector.

Anthropic says Claude may want to see your ID: The revelation that Claude could request identity verification raises significant questions about AI trust frameworks, consent, and data privacy standards globally and in SEA.

OpenAI launches initiative to patch open-source bugs: The new programme aims to identify and fix security vulnerabilities in widely used open-source software, a move that could benefit the broader developer ecosystem in SEA.

Ubisoft co-founder Claude Guillemot dies in plane crash: The death of one of the gaming industry’s founding figures marks a significant loss for the global tech and entertainment community.

Shareholders sue Uber’s board over sexual assault incidents: A lawsuit targeting Uber’s board over its handling of safety incidents raises governance accountability questions relevant to platform companies operating across Southeast Asia.

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CYBERSECURITY

After a bank cyberattack, restoring the wrong data is the real risk: A sharp analysis of post-breach recovery failures argues that corrupted data restoration poses a greater threat to financial institutions than the initial attack itself.

Unpatchable flaw in Apple chips opens door to iPhone jailbreak: Researchers have identified a hardware-level vulnerability in Apple silicon that cannot be fixed via software update, exposing millions of devices, including those widely used across SEA, to potential exploits.

WazirX bets on AI futures trading after US$235M hack: The embattled Indian crypto exchange is pivoting to AI-driven futures products as part of its comeback strategy following one of Asia’s largest crypto security breaches.

Why cyber risk ownership is SEA’s biggest leadership blind spot: Leaders across the region continue to delegate cybersecurity to IT teams rather than treating it as a board-level strategic concern, leaving organisations structurally exposed.

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SEMICONDUCTOR

Qualcomm nears deal for AI chip startup Modular: Qualcomm is close to acquiring Modular, a move that would bolster its AI inference capabilities and intensify competition with Nvidia and AMD in the on-device AI chip market.

Samsung unveils industry’s fastest UFS 5.0 storage solution: The new UFS 5.0 chip delivers double the speed of its predecessor and is designed to power next-generation on-device AI applications across smartphones and edge devices.

Alibaba chip unit raises registered capital by US$148M: The capital injection into Alibaba’s semiconductor arm signals a renewed push to build homegrown chip capabilities amid sustained US export restrictions on advanced technology to China.

SpaceX’s Colossus data centre raises reflection concerns: Elon Musk’s AI data centre is drawing scrutiny over its environmental and operational footprint, a timely reference point as SEA governments approve large-scale AI infrastructure investments.

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AI

The AI divide in the Philippines started before AI: The piece argues that structural inequalities in digital access and education mean the Philippines risks amplifying existing gaps rather than closing them through AI adoption.

AI agents are joining the workforce; inclusion must follow: As agentic AI becomes embedded in enterprise workflows, technologists are calling for diversity and inclusion principles to be built into AI agent design from the outset.

SEA’s AI momentum outpaces its institutional maturity: A sobering assessment finds that Southeast Asia’s rapid AI adoption is running ahead of the governance frameworks, talent pipelines, and infrastructure needed to sustain it.

Singapore’s AI opportunity is now about discipline, not adoption: The city-state has moved pastthe question of whether to adopt AI and must now focus on building the organisational rigour to deploy it effectively and responsibly.

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THOUGHT LEADERSHIP

VC liked you; that’s not the same as yes: A candid examination of how founders misread investor signals during fundraising, confusing positive engagement for commitment, a common and costly mistake in the SEA startup circuit.

When execution is free, the brief becomes the product: As AI commoditises delivery, strategy and clarity of thinking become the scarcest and most valuable inputs, a fundamental shift in how founders and operators should think about their roles.

The next startup opportunities are forming around control: The argument is that as AI automates efficiency gains, the next wave of valuable startups will be those that give users and organisations meaningful control over automated systems.

How AI stocks are stealing billions from crypto: As institutional capital rotates from crypto into AI equities, the piece examines what this structural shift means for crypto valuations and the investor appetite for digital assets in SEA.

Why tracking Bitcoin ETFs matters for SEA investors: Bitcoin ETF flows are becoming a reliable proxy for institutional sentiment toward crypto, offering SEA investors a clearer signal amid market volatility.

Social impact funding needs a common language, not more capital: The piece contends that impact investing in SEA is held back less by a lack of funds than by the absence of shared metrics and definitions across funders and founders.

The Eisenhower Matrix, Maslow, and the goals you set yourself: A reflective essay challenges founders and operators to question whether their goal-setting frameworks serve genuine priorities or simply replicate conventional ambition.

The post Ecosystem Roundup: The agentic commerce trust gap no one wants to fix appeared first on e27.

Singapore has set an ambitious target: 100,000 “AI-bilingual” workers by 2029. The goal signals a broader reckoning with how AI is reshaping the professional workforce — not merely as a productivity tool, but as a capability that demands a new kind of literacy. Yet as training programmes multiply and certification frameworks take shape, a harder question is emerging: what does AI bilingualism actually require in practice?

“AI bilingualism means having enough domain expertise and AI fluency to actually direct, evaluate, and push back on what AI gives you,” says Felicia Tan, Director of Tribe Academy, in an email interview with e27.

The distinction matters. Faster, more polished output is already well within reach for most professionals. The ability to spot where that output is wrong — or quietly dangerous — is proving far more elusive.

Tribe Academy offers expert-led training in areas including AI and blockchain to further bridge Singapore’s talent gap. In our conversation, Tan reveals the blockers that many corporations in Singapore face in embracing AI–and what to do about it.

The following is an edited excerpt of the conversation.

Also Read: 5 Seoul startups made their Southeast Asia debut at Echelon Singapore 2026 under the SBA pavilion

MOM’s latest survey shows 70 per cent of Singapore companies still have not adopted AI for work, a striking number given how much policy attention has gone into this space. In your experience working with corporate clients, what’s the real blocker?

If you spend a lot of time in tech circles, it can feel like everyone is already using AI. But outside that bubble, many organisations are still at the stage of observing, experimenting cautiously, or waiting to see clearer proof of value before changing how work gets done.

Everett Rogers gave us the Diffusion of Innovations curve decades ago, and it remains one of the most useful lenses for moments like this. The theory has long shown that every major shift moves through stages. Innovators, early adopters, early majority, late majority, then laggards, each arriving on their own schedule, for their own reasons. Right now, AI still sits heavily between the early adopterand early majority phase for many Singapore companies.

From the perspective of early adopters, it can feel like progress is slow. But we also need to recognise the scale of behavioural change being asked of the workforce. The oldest members of our working population in Singapore today entered their careers roughly 30 years ago, in the mid-1990s … Entire careers were built around ways of working that rewarded precision, hierarchy, and predictability.

AI changes not just the tools people use, but the nature of how work gets done. That transition naturally takes time, especially at workforce scale. Policies and national initiatives help create momentum, but cultural and operational change inside organisations has always moved slower than headlines.

One main blocker we are seeing with AI adoption is that it is still highly siloed and deeply individual. We see individuals attending our programmes who bring these skills back, but only to their personal chat windows. Someone on the team discovers a prompt that saves them two hours a week, and they quietly use it, and nobody else knows. Someone in HR uses a new AI tool for meeting summaries but the knowledge stays private. There is no institutional memory layer or shared playbook that captures what’s working.

Also Read: The great rotation: How AI stocks are stealing billions from crypto

So you get a patchwork where a few power users produce impressive outputs, while everyone else is doing things roughly the way they always have. The gain will live and die with the individual. For organisation-wide impact, a deliberate redesign of workflows, KPIs, or operating models will need to follow either through top-down directives or a conscientious effort by the entire staff.

The next blocker is arguably the most honest one, i.e. if it isn’t broken, why fix it? Not everyone is a productivity advocate and lies awake worrying about workflow inefficiency. Sure, some firms are redesigning roles and creating new AI-related positions. But AI is not visibly taking anyone’s job tomorrow. Then reasonably, as human beings, we tend to stay with what works, that is, no change is needed.

The third blocker is structural among those who have attempted to look into AI implementation. The most commonly cited constraints are high implementation costs and lack of in-house expertise. The tools exist. The willingness, in many cases, exists too. But the bridge between “I’ve heard of AI” and “We’ve redesigned our workflow around it” is still too long and too expensive for most SMEs to cross without support.

There’s a tendency among early movers to look at policy timelines and grow impatient. But policy takes time to land. The government has announced a new Tripartite Jobs Council to support employers and employees in AI adoption, alongside access to free premium AI tools for Singaporeans taking selected AI courses.

The reality is that a Budget announcement in February does not transform workforce behaviour by April. There will always be a lag between national intent and organisational habit change. The real test is what companies do in that gap. Grants and national initiatives can reduce the risk of taking the first step, but they cannot redesign workflows on behalf of every employer.

Also Read: Agentic commerce’s dirty secret: the data powering AI purchases is often wrong

There’s been a lot of hiring around “prompt engineers” over the past two years. Is that the right unit of skill to be built for? What’s the capability that actually drives business value that most job descriptions and course catalogues are still missing?

Good prompt engineering is underrated. It was the first core skill that emerged when Generative AI became accessible to the general public, and it remains foundational.

The fact that leading AI companies are still publishing prompt engineering guides and running 101 courses around it tells us something. The practical case for why it matters more is important as models get more powerful. The newer reasoning models consume significantly more tokens, especially when you are building complex workflows or automating multi-step processes. If you do not know how to construct a tight, well-structured prompt, you will burn through credits at an alarmingly fast rate.

Extrapolating this across a team running dozens of automated workflows, and it becomes economically untenable. It is a boring skill compared to flashy AI apps and dashboards, but knowing how to communicate with AI systems precisely will save companies enormous amounts of money and frustration over time.

Prompt engineering remains a useful skill to develop, but its real value is as a foundation for broader AI capability, not as the end goal. Prompt engineering gets one to a useful first draft. What you do with that draft … is one that most job descriptions and course catalogues are still fumbling to articulate, but what is going to derive the most business value.

So, if I were advising an enterprise on what capability to actually build for, it would be this: Workers who have developed strong prompt discipline as a baseline habit, and who pair it with critical thinking to know when the model is leading them somewhere wrong. The future is probably less “prompt engineer” and more “AI-native operator”.

Also Read: Why Cyber Risk Ownership Is Southeast Asia’s Biggest Leadership Blind Spot

If you could redesign one thing about how Singapore is approaching workforce AI upskilling right now, what would it be?

If I could redesign one thing about how Singapore is approaching workforce AI upskilling right now, I would shift the focus from primarily funding structured training to creating a much stronger bridge between training and rapid on-the-job application.

Many companies still see upskilling as “time away from real work”. To close this gap, we need structured training to remain the foundation in building mental models and tool confidence, but we must also ensure it then quickly flows into real application. Specifically, companies should set aside dedicated hours each month for employees to test AI on actual tasks, just like how R&D time is ringfenced in tech companies and “timetabled time” is set aside for teachers to dedicate time to innovation and professional development.

Policy can reinforce this by tying enhanced grants to organisations that implement and report on these pilots, with even stronger support when they become sustained initiatives rather than one-off efforts. Once organisations have a core group of upskilled champion users, they should guide the rest of the team to start small with specific tasks, such as shortlisting documents, summarising meeting notes, or automating a two-part workflow.

The goal is to learn by doing something real and low-stakes. Have employees treat early failures as cheap tuition. Just like in the early days of the internet, nobody expected the first company website to generate revenue immediately.

This approach aligns with one of Singapore’s strongest policy philosophies: reducing the downside ofexperimentation. Our grants, co-funding, and training subsidies were never designed to guarantee perfect outcomes, they exist to make the first step less risky and encourage early action.

By redesigning the system this way, we can turn awareness and training into genuine productivity gains and keep Singapore’s workforce truly competitive.

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Image Credit: Cash Macanaya on Unsplash

The post Tribe Academy’s Felicia Tan: Why good prompt engineering and critical thinking are keys to AI bilingualism appeared first on e27.

H3 Zoom, a Singapore‑based deeptech startup building AI‑driven inspection and asset‑intelligence software, has closed an oversubscribed Series A round of US$3.6 million.

The financing was led by JRE Ventures, the corporate venture arm of Japan’s East Japan Railway Company, with participation from SGInnovate, M7 Holdings, Moringa Ventures, and Lotus One Investment, besides an AngelCentral member syndicate.

Also Read: H3 Zoom lands US$1.8M to accelerate AI-powered building inspections in Japan, HK

The cash will bankroll H3 Zoom’s push across Asia, with explicit emphasis on Japan, Hong Kong SAR, Singapore, and Southeast Asia. The company says it will invest in product development, engineering hires, enterprise go‑to‑market execution and integrations across building and infrastructure lifecycles.

Why investors are paying attention

H3 Zoom has spent the past decade combining computer vision, proprietary vision‑language models, drones and robotics‑assisted capture into a single inspection workflow. The result is a platform that converts photographic and sensor inputs into structured, standards‑aligned reports and analytics, a shift from manual, fragmented inspections toward traceable, repeatable processes.

Investors tell a similar story: infrastructure owners in Asia face an ageing stock of assets, tighter budgets, and skills shortages, creating a market for scalable inspection technologies. For corporate strategic investors such as JRE Ventures, the appeal is both defensive and strategic, ensuring safer operations across rail, station and commercial assets while opening commercial ties across Asian markets.

“Through this investment, we aim to accelerate H3 Zoom’s business expansion and proof‑of‑concept activities in the Japanese market, while exploring broader collaboration opportunities across Southeast Asia,” said Junichi Eto, Managing Director at JRE Ventures. His comment highlights the importance of Japan as a commercial beachhead for the company, and the potential for tech transfer into regional partners and operators.

What H3 Zoom actually sells

H3 Zoom’s headline products –Façade Inspector and Interior Inspector — are focused on reducing inspection time, cutting operational and access costs, and lowering reliance on labour‑intensive work‑at‑height activities. Using drones for data capture, AI for defect analytics and standardised reporting workflows, the company says it helps customers adhere to regulatory frameworks such as Singapore’s Building and Construction Authority periodic façade inspection regime.

Also Read: Transforming asset inspections: How WaveScan’s smart sensors and AI are shaping predictive maintenance

In practical terms, that matters for Southeast Asia. Cities across the region are racing to upgrade ageing building stocks and transport infrastructure while facing tightening labour markets. Local authorities and facility owners increasingly demand verifiable inspection records, and insurers are looking for standardised evidence of maintenance. That creates a commercial runway for software that not only detects defects but also ties findings into asset management systems and maintenance workflows.

Regional traction and repeat business

Investors pointed to H3 Zoom’s customer traction and repeat business as reasons to double down. AngelCentral’s member‑led syndication that topped up the round after an earlier first close was singled out as an important validation of the company’s regional momentum.

“I was not only impressed by the concept, but most of all by the traction the company had already,” said Marnix Beugel, the AngelCentral syndicate lead. The remark underscores a common investor filter in Southeast Asia: demonstrable, recurring revenues from repeat customers often outweigh speculative product roadmaps.

Product roadmap: AI co‑pilot and multimodal workflows

With fresh capital, H3 Zoom plans to accelerate an “AI Engineering Co‑Pilot” and multimodal inspection features combining 360‑degree imagery and voice notes, alongside enterprise‑grade APIs and robotics‑assisted capture. The aim is to make inspections faster and to surface actionable issues for engineers more consistently, turning inspection outputs into measurable maintenance outcomes.

Shaun Koo, H3 Zoom’s founder and CEO, framed the funding as a validation of the company’s mission. “With this capital, we will accelerate our AI roadmap, deepen enterprise integrations, and scale across key Asian markets where infrastructure safety, asset resilience and inspection productivity are becoming increasingly important,” he said.

Competition and the wider market

H3 Zoom operates in a crowded but fragmented space. Startups, system integrators and established engineering firms are all experimenting with drone capture, AI analytics and robotic inspection. Where H3 Zoom hopes to differentiate is through integration: combining capture hardware, proprietary AI models and enterprise workflows that align with regulatory standards.

Also Read: How Japan can empower a new wave of SEA startup innovation

For Southeast Asian operators, the practical considerations are often interoperability and ease of deployment. Systems that bolt on to existing asset management processes and can deliver immediate compliance documentation will likely win the earliest deployments. H3 Zoom’s focus on standards‑aligned reporting in Singapore is therefore a strategic proof point for expansion into nearby markets like Malaysia, Indonesia and the Philippines.

The outlook

The Series A puts H3 Zoom in a stronger position to pursue contracts with asset owners, facility managers and public agencies across Asia. With backing from a mix of strategic (JR East), public deeptech investor (SGInnovate) and regional VCs, the company gains not only capital but commercial channels into Japan and Southeast Asia.

As infrastructure in the region ages and labour costs rise, demand for verification, traceability and decision‑grade inspection data is unlikely to fall. The question for H3 Zoom will be whether it can convert its product depth and early traction into scaled enterprise contracts, and whether its integrations and APIs make it the default “operating layer” for inspection intelligence across Asia‑Pacific.

If it succeeds, the result could be less about replacing humans than about making inspections safer, faster and more auditable — an outcome that resonates as much with regulators and insurers as with engineers on the ground.

The post From façades to railways: H3 Zoom raises US$3.6M to commercialise AI inspection tech across SEA, Japan appeared first on e27.

A DWF Labs Research report estimates that more than US$31 billion of tokenised assets, excluding stablecoins, now sits on-chain, up 50 per cent this year. Growth has been led by US Treasuries and private credit, as asset managers digitise familiar products for blockchain-based distribution.

The more revealing figure is how little of that capital is being used in decentralised finance. Only around US$3 billion, or roughly 10 per cent of tokenised assets, is active as DeFi total value locked.

Also Read: The future of investing isn’t TradFi or DeFi: It’s tokenised, transparent, and built for the next billion

Large tokenised Treasury products such as BlackRock’s BUIDL, WTGXX, and Franklin Templeton’s BENJI are estimated to see fewer than 30 transfers a month.

The bottleneck is market structure

For Southeast Asian fintech founders, exchanges and infrastructure builders, the distinction matters. Tokenisation alone does not create liquidity, access or capital efficiency. Those outcomes depend on pricing, redemption and market access, where the current stack remains weak.

DWF Labs identifies three barriers. Pricing for private credit and real estate is too slow, with many products relying on net asset value updates that arrive daily at best. That makes it difficult for market makers to quote size without wide spreads.

Redemption is also cumbersome. The promise of blockchain finance is instant settlement, but many tokenised assets still take days to redeem because underlying assets and counterparties operate on legacy timelines. On-chain liquidity is too thin for institutional trades, while over-the-counter markets remain fragmented.

Regulation further limits composability. Transfer restrictions, know-your-customer checks and accreditation requirements are common across institutional issuances. These controls may be necessary for regulated assets, but they sit uneasily with permissionless DeFi protocols that rely on open participation and automated collateral flows.

“Liquidity is the binding constraint on scaling tokenisation on-chain,” said Andrei Grachev, Managing Partner at DWF Labs, pointing to the need for real-time pricing, instant redemption and deeper secondary markets.

Who captures the value

So far, the biggest winners have been issuers and asset managers that control distribution. Crypto-native infrastructure providers, including lending protocols, oracles, market makers and redemption venues, have captured a smaller share despite building much of the plumbing.

Also Read: What June 1 changed for Asia’s stablecoin rails

That is beginning to shift. Maple Finance has drawn more than US$3.6 billion in TVL by using tokenised credit as stablecoin collateral through syrupUSDC and syrupUSDT. The wrapper model can bring less liquid assets into DeFi lending markets, although it also introduces allocation, disclosure and default risks.

Oracle providers are another critical layer. Pyth and Redstone are developing around-the-clock pricing infrastructure for tokenised stocks and commodities, a prerequisite if market makers are to quote tighter spreads on assets that previously depended on slower reference prices.

Redemption infrastructure is also emerging. Symbiotic’s Liquid Lane proposes shared vaults where market makers compete through a request-for-quote layer to price redemption discounts. Figure is taking a vertically integrated route by combining origination, secondary price discovery and settlement, including more than US$21 billion in home equity lines of credit originated on Provenance and YLDS, an SEC-registered yield-bearing stablecoin.

The next opportunity is not another Treasury wrapper

The report points to two areas where the next wave of value may emerge: non-US dollar debt and yield-bearing access to commodities and equities.

More than 94 per cent of tokenised assets remain US dollar-denominated, even though non-US dollar sovereign bonds account for more than 45 per cent of the traditional global fixed-income market. Emerging-market debt is especially relevant for Asia-facing investors because the yield gap is wider than in US Treasury products. Brazilian real bonds yield around 10 per cent, while Turkish lira bonds yield around 15 per cent, with non-deliverable forwards available to hedge currency risk.

The same logic applies to regional private credit across APAC and MENA, where borrowers may face higher funding costs and investors are searching for transparent, programmable access. For Southeast Asia, tokenisation could become more than a digitised fund wrapper if infrastructure can handle credit assessment, currency risk, servicing and secondary liquidity.

Commodities and equities offer a different opportunity. Tokenised commodities have generated more than US$4.8 billion on-chain, with US$4.8 billion on-chain, with US$ 90.7 billion in first-quarter 2026 activity. Tokenised equities have grown to more than US$1 billion and 185,000 holders in a year. These products show retail demand for price exposure, but they do not naturally generate yield.

Also Read: Southeast Asia should take note: Bitcoin mining is no longer an industrial game

Protocols that can safely layer yield onto these assets, through stablecoin collateral, lending markets or options strategies, are likely to capture stickier users than platforms that simply list tokenised instruments.

Tokenisation’s first act was about issuance. Its second will be about utility. Until assets can be priced in real time, redeemed quickly and traded in sufficient depth, much of the capital brought on-chain will remain idle. For Southeast Asia’s builders, the opportunity is less about announcing another tokenised product and more about solving the market plumbing that makes those products useful.

The post Tokenised assets have moved on-chain. The liquidity has not followed appeared first on e27.

The Philippines is quietly building one of Southeast Asia’s most diverse AI startup ecosystems. While the country has long been recognised for its tech-enabled services sector, a new generation of homegrown companies is now moving up the value chain — building original AI products across logistics, healthcare, gaming, gig work, and customer experience.

From Senti AI’s decade-long work in Filipino natural language processing to newer entrants like Matcha tackling informal labour verification and Safe targeting online scam prevention, these startups reflect both the country’s unique market realities and its global ambitions. Several have already drawn backing from top-tier investors, including a16z, Y Combinator, Bain Capital, and Peak XV.

Also Read: The AI divide in the Philippines began long before AI

Here is a look at 15 emerging Philippine AI startups that are worth watching and a sign of just how much the local AI landscape has matured.

Expedock

ChatGenie

CAWIL.AI

Tenext.ai

Ludo Launchpad 

Matcha

Safe

Clout Kitchen

Sourcy

AIFirst

Intelligent AI Solutions

Serbiz

Adapsense

Dashlabs.ai

Senti AI

The post Philippine AI is no longer a footnote. Here are the 15 startups proving it appeared first on e27.

Bitcoin currently trades at US$63,386.87 after experiencing a 1.24 per cent decline over the past 24 hours. This downward movement mirrors a broader one per cent contraction in total cryptocurrency market capitalisation. These short-term price fluctuations are predictable reactions to external macroeconomic shocks rather than systemic failures.

The current sell-off lacks any crypto-specific negative catalyst. Traditional institutional selling pressure and escalating global tensions dictate the immediate price action. We must separate the fundamental progress of distributed technology from the temporary noise of global political theatre.

The primary catalyst driving this risk-off sentiment is the collapse of ceasefire negotiations between the United States and Iran over the weekend. New military warnings from United States President Donald Trump and Tehran’s subsequent decision to close the Strait of Hormuz again severely shook recent optimism about technology. This geopolitical friction immediately triggered a reversal across global equity and commodity markets. United States equity futures fell sharply following the Juneteenth holiday.

S&P 500 futures dropped 0.5 per cent while Nasdaq 100 contracts declined 0.7 per cent. Asian markets reflected this same anxiety. The Japanese Nikkei 225 opened slightly lower at 71,067.15, then fluctuated up to 72,133.88 as overnight futures provided local support. South Korea’s KOSPI dropped more than 1.1 per cent in morning trading, with chip giant Samsung Electronics leading losses, sliding over three per cent. Australia ASX 200 also slumped early as investors digested weekend energy transport disruptions. Heavyweight BHP faced a steep sell-off following massive cost overruns. Bitcoin simply reacts to this same global liquidity contraction.

Also Read: 81% correlated with gold: Is Bitcoin just another macro derivative now?

Commodity and currency markets highlight the exact nature of this macroeconomic stress. Crude oil surged amid severe supply chain anxiety. Brent crude rose over one per cent to top 81.50, and West Texas Intermediate jumped nearly three per cent to trade near $78.This\; energyshockstrengthenstheUS$ against most major currency peers as investors seek safe-haven assets. The British Pound weakened 0.2 per cent on widespread speculation that United Kingdom Prime Minister Keir Starmer might resign following political defeats. Investors clearly demand stability.

Beyond immediate geopolitical triggers, markets also brace for the crucial United States Core PCE inflation release on Thursday. The Federal Reserve under new Chair Kevin Warsh recently executed a hawkish pivot. Policy paths now hint at potential 2026 interest rate hikes. This traditional financial tightening directly pressures risk assets, including cryptocurrencies. The Nasdaq-100 quarterly rebalance takes effect today. The index added major tech players such as CoreWeave and Rocket Lab while removing legacy firms such as Charter Communications. These structural shifts in traditional equity markets force institutional portfolio managers to rebalance their broader risk exposure, inadvertently dragging digital assets into the sell-off.

We must also address the persistent institutional selling pressure weighing heavily on Bitcoin. United States spot Bitcoin funds recorded a record US$6.35 billion in net outflows over the past 30 days. The daily pace of these outflows recently slowed, but this persistent drain removes a massive source of traditional demand from the market. I maintain that integrating digital assets into traditional financial wrappers introduces legacy market behaviours into our ecosystem.

Traditional financial tests, such as the Howey test, remain entirely unsuitable for evaluating these distributed crypto systems. Regulators fail to understand that digital assets operate on fundamentally different architectural principles. When traditional institutions face geopolitical shocks or margin calls in equity markets, they initially liquidate their most liquid alternative assets. Bitcoin currently absorbs this traditional market fragility. The asset reacts to macro risks and a withdrawal of institutional capital rather than any fundamental deterioration in network activity. This dynamic shows that digital assets remain tethered to the whims of global equity markets until we achieve true decentralisation.

Also Read: Why tech giants are crashing while Bitcoin surges to US$67,000

Technical indicators and derivatives data reveal a market structure that remains weak but entirely orderly. Bitcoin currently trades below its seven-day simple moving average of US$63,823 and its 30-day simple moving average of US$64,037. This positioning confirms short-term bearish momentum across all major timeframes. The Relative Strength Index reading of 30.06 shows the asset sits in oversold territory without reaching extreme capitulation levels. The derivatives market provides further clarity on ecosystem health.

Total open interest fell by 4.56 per cent in the last 24 hours, while Bitcoin liquidations dropped by an impressive 46.54 per cent. These numbers signal lower speculative leverage and eliminate the risk of an immediate squeeze. The market unwinds excess leverage in a controlled manner rather than experiencing a chaotic cascade of mandatory selling. This orderly deleveraging creates a healthier foundation for potential recovery. Speculators cleared out weak positions, leaving only dedicated capital in the market to support future price discovery.

Traders examining the near-term market outlook must focus entirely on specific price levels to gauge the next directional move. The critical support zone is at US$63,200, representing the recent 24-hour low. If buyers successfully defend this zone, a rebound toward the swing high resistance at US$64,506 becomes highly probable. The path of least resistance remains downward unless Bitcoin fund flows turn positive.

A definitive break below the US$63,200 support could trigger a quick test of the psychological US$62,000 level. The bias remains neutral to bearish until Bitcoin reclaims and holds above the US$64,500 resistance area. We must also monitor any escalation in the situation in the Strait of Hormuz or sudden reversals in daily Bitcoin fund flows. 

This short-term bearish pressure ultimately tests network resilience and separates fleeting speculative capital from genuine believers in distributed financial infrastructure. We currently stand on the precipice of a truly human-focused, highly practical application layer that transcends legacy market volatility.

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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.

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Banks often treat cyber recovery and regulatory reporting as separate workstreams. One team restores services. Another drafts the incident report. That split may look tidy, but in practice, it creates risk.

Both activities deal with the same problem. Facts are incomplete, pressure is immediate, and decisions must be made before anyone fully understands what has been damaged, altered, or trusted too quickly. A bank can bring systems back online and still restore a corrupted operating state. It can notify regulators quickly and still create a record it later struggles to defend. The real challenge is not speed alone. It is disciplined speed under uncertainty.

Regulators are increasingly recognising this reality. They are moving towards earlier notification with structured follow-up instead of waiting for perfect hindsight. In the United States, federal banking agencies require notification to the primary regulator as soon as possible and no later than 36 hours after the bank determines that a qualifying incident has occurred. Under DORA, firms must submit an initial notification, then intermediate reports as the incident changes materially, and then a final report. In the United Kingdom, the FCA’s finalised guidance issued in March 2026 also accepts an indicative root cause during the initial and intermediate phases, with confirmation expected later.

The most dangerous recovery is the fast but false recovery

A cyber event is not over when an application starts responding again. In a bank, the harder question is whether the institution has restored a trustworthy state.

A payment platform may be available while still operating on corrupted queues. A servicing system may be live while drawing on altered customer records. An authentication layer may be back while still containing poisoned privilege assignments. A reconciled ledger may look stable even though upstream dependencies remain inconsistent. NIST and CISA guidance both point to the same principle. Recovery is not just about bringing systems back. It is about restoring operations and data that the organisation can trust.

Banks therefore, need to be more precise in their language. The goal is not service restoration alone. It is state restoration. That means restoring data state, entitlement state, rules state, model state, queue state, and reconciliation state to a version the institution is prepared to stand behind. Banking systems do not only process transactions. They preserve institutional truth. Once that truth is in doubt, speed without integrity creates a new layer of risk.

Also Read: The truth behind the CLARITY Act lobby blitz: Crypto to the moon or banks compromise

Recover to a certified state, not merely the last available state

Many recovery plans still assume that a clean rollback point exists and that operational pressure will allow the bank to trust it quickly. In reality, corrupted states are often harder to isolate than outages. Damage may have spread across data stores, replication layers, configuration histories, privileged access paths, and operational decisions taken after the initial compromise.

NIST’s data integrity guidance is valuable because it goes beyond generic backup language. It stresses the need to consider integrity at the application and business process levels, to test backups through end-to-end restores, and to maintain a recovery catalogue showing which copies have been scanned and whether older copies may themselves be poisoned.

Banks should push that logic further. Critical services should not reopen simply because infrastructure has been rebuilt. They should reopen because a recovery authority has certified that the restored state is coherent enough for the bank’s control environment, customer duties, and regulatory obligations. The real question is not “can we restore?” but “which version of reality are we restoring, and what evidence makes us trust it?”

Reporting early does not mean pretending to know more than you do

Banks often feel trapped between two bad options. Either they delay notification while chasing confidence they will not have in the first few hours, or they report early with more certainty than the evidence supports.

Both are weak responses. Delay is not discipline. Overstatement is not defensibility.

Current regulation is actually more practical than many firms assume. DORA is built around staged reporting through initial, intermediate, and final submissions. The FCA’s latest guidance similarly distinguishes between early and later phases. The message is clear. Regulators increasingly expect early situational awareness followed by maturing updates, not a perfect narrative delivered too late.

The banks that handle this well do not report certainty. They report bounded truth. They distinguish what is confirmed, what is strongly suspected, what remains unknown, what actions have been taken, and what assumptions may still change. That is usually the most defensible position available in the opening phase of an incident.

Also Read: From policy to capital: How development banks are driving the climate x health agenda

The first report should state facts, impact, and decision

Many first reports fail because they try to be too complete too early. Forensic theory, customer impact, technical noise, and management reassurance all get blended into one unstable document.

A stronger first report is narrower. It should state what the bank knows about service disruption, data integrity, confidentiality exposure, and affected business services. It should explain what threshold triggered the notification and what actions have already been taken to contain the incident. It should separate confirmed impact from potential impact. It should record the current operating posture, whether services are suspended, partially restored, or running under restricted controls. It should also state the main uncertainties in plain language.

That is much closer to how current frameworks are written. Regulators want timely and structured information that shows material impact, current control posture, and the institution’s response, not an artificial sense of closure.

Recovery and reporting need one evidential spine

The biggest operational mistake is to let recovery teams and reporting teams build separate versions of the incident.

When that happens, technical teams speak in hypotheses, restoration checkpoints, and system states, while reporting teams speak in regulatory thresholds, customer impact, and executive language. Each account may make sense on its own, but together they create a contradiction. The bank then ends up with one account of what was restored, another of what was reported, and a third of what customers later experienced.

Banks need one evidential spine feeding both recovery and reporting. It should capture timestamped facts, material decisions, restoration checkpoints, confidence levels, changed hypotheses, customer impact estimates, and evidence sources. That is what allows the bank to explain later why it made the calls it made while the facts were still moving.

Also Read: Trump vs banks: How stalled crypto legislation is crushing market sentiment

Final thought

Cyber recovery in banking is becoming less about bringing systems back and more about deciding which institutional truth can safely be trusted again.

That is why material incident reporting and safe recovery should not be treated as separate disciplines. Both are exercises in disciplined honesty under uncertainty. The bank has to say what it knows before the picture is complete, and it has to restore only what it is willing to defend later.

The institutions that will do this well are not the ones that sound most confident in the first 24 hours. They are the ones that recover without replaying corruption, report without pretending to know the unknowable, and show afterwards that their early judgment was careful enough to deserve trust.

—

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 After a bank cyberattack, the real risk is restoring the wrong version of the truth appeared first on e27.

Southeast Asia’s AI scene is sprinting ahead, and Thailand is quietly becoming its laboratory. From generative spatial design and energy‑saving AIoT to sovereign Thai language models and an “AI nose” that tastes food, a new wave of startups is turning local problems into global products.

This list rounds up 15 homegrown companies that typify the region’s pragmatic, product‑first approach: enterprises solving real operational pain points for banks, CDMOs, contact centres and architects, not just flashy demos. Some are scaling fast with fresh funding; others are proving deeptech chops on international stages.

Also Read: AI is a game-changer, and here’s how your business can use it to win

Read on for a curated snapshot of who’s shipping, what they actually do, and why investors and enterprise customers are paying attention. If you’re tracking AI adoption in the region, these founders are the ones rewriting the playbook, one deployable model at a time.

WiseSight

Ricult

Zwiz.AI 

Sertis

DataWow

Spacely AI

MUI‑Robotics

OsseoLabs 

iApp Technology

BOTNOI Group

Gowajee

AltoTech Global

GuardianGPT

Wisible

AIRA

 

The post 15 Thai AI companies betting on products, not hype appeared first on e27.

The post From support inbox to signal feed: Inside the AI workflow that won at Echelon Singapore 2026 appeared first on e27.

The post What nine AI workflow submissions reveal about Echelon Singapore’s builder pipeline appeared first on e27.