The question “Is the AI industry profitable?” has two correct answers, and they point in opposite directions. At the chip-design and leading-edge-fabrication layers, AI is already one of the most profitable industries in commercial history. At the layers the market calls “AI”, frontier model labs, GPU-rental builders, and most applications built on someone else’s model, it is among the most loss-making activities ever financed.

Both statements are true. The investment question sits in the distance between them.

The reason is mechanical. Across the AI stack, the cost of intelligence is falling rapidly. But a falling cost only becomes profit somewhere. Whether that decline lands as margin, as a lower price to the customer, or as a transfer to a supplier depends on one question: who owns the bottleneck between the falling cost and the price the customer will pay?

Where a firm owns that bottleneck, it keeps the cost decline as margin. Where it owns none, competition forces the decline through. Walk the AI value chain from chips to applications, and the pattern is already visible. Profit sits where a pass-through is blocked. It evaporates where competition lets pass-through run free.

Start with the two companies that keep the money. NVIDIA reported fiscal-2026 revenue of US$215.9 billion, GAAP operating income of US$130.4 billion, and GAAP net income of US$120.1 billion, a net margin of nearly 56 per cent. TSMC earned 2025 net income of US$55.2 billion on revenue of US$122.4 billion, a 45.1 per cent net margin. Together, Nvidia and TSMC booked roughly US$175 billion of net profit in their latest fiscal years.

This is not a forecast. It is where AI profitability already exists.

Both companies sit behind gates that the rest of the stack must pass through. NVIDIA’s moat rests on CUDA, networking, scale, and the difficulty of coordinating around an alternative. TSMC’s moat is harder still: leading-edge fabrication is gated by physics, capital, yield learning, and process knowledge that takes years to reproduce. These are not normal suppliers. They are toll collectors.

The cloud layer is more ambiguous. AWS, Microsoft Azure, and Google Cloud are large, profitable businesses. AWS earned a 37.7 per cent operating margin in the first quarter of 2026, and Microsoft’s Intelligent Cloud has run margins in the low 40s. But hyperscaler free cash flow is being consumed by the AI build-out, and the cloud owners are trying to escape Nvidia’s toll through custom silicon. Amazon’s Trainium, Google’s TPUs, and Microsoft’s Maia are attempts to become bottleneck owners rather than resellers of someone else’s bottleneck.

Also Read: The AI economy is moving faster than our institutions

Where a cloud owner runs its own silicon, it can keep more margin. Where it buys Nvidia capacity, finances data centres, and rents compute to model labs, its economics compress. The cloud business is profitable, but AI infrastructure may not be unless demand arrives fast enough and custom silicon works well enough.

The neoclouds show what happens when revenue grows without a bottleneck. CoreWeave more than doubled first-quarter 2026 revenue to US$2.08 billion and reported a 56 per cent adjusted EBITDA margin. But adjusted operating margin was only 1.0 per cent, and GAAP net loss was US$740 million, with quarterly interest expense of US$536 million. Depreciation on GPUs and debt service consume the economics. CoreWeave buys Nvidia hardware at market prices, finances it with borrowing, and rents capacity into a competitive market. It owns no gate.

The frontier labs invert the popular intuition. OpenAI’s annualised revenue run-rate was roughly US$20 billion at the end of 2025. Anthropic reportedly reached around US$30 billion in April 2026 and about US$47 billion by late May. The growth is real, even if the figures are reported rather than audited. But revenue is not profit. OpenAI’s gross margin has been reported to be around one-third, constrained by inference costs, and internal projections reported publicly pointed to a multibillion-dollar loss in 2026.

Through the Abundance Economics lens, the labs are not toll collectors. They are in demand. A large share of their revenue flows upstream to chips and cloud and lands there as margin. The model itself is becoming less defensible because two forces push price down at once: open-weight models keep closing the capability gap, and the cost of fixed model performance keeps falling. In a layer with low switching costs and credible substitutes, falling input costs cannot be retained. Competition forces it through. That is why a lab can scale revenue explosively and still lose money.

The application layer needs more care. “AI apps” are not one category. Thin wrappers over frontier APIs own no gate and are likely to be crushed. Embedded workflow systems can be different because they control customer data, procurement position, operating processes, or a regulated context. Distribution-owned applications can also hold margin where they already own the user relationship.

Also Read: Give physical AI a soul: Why your voice AI still feels like a bot

Palantir is the clearest example. It is not just “an AI app.” It is an embedded data-and-workflow layer inside government and enterprise operations, and that position can behave like a bottleneck. By contrast, implementation consultants capture demand but earn consulting economics. Accenture may book billions in generative-AI work, but its overall operating margin remains around 15 per cent.

• The first capital-allocation implication is simple: do not price the AI stack as one trade. NVIDIA, TSMC, hyperscalers, neoclouds, model labs, workflow software, and thin apps have different economics because they sit in different places in the pass-through chain.
• The second implication is that revenue is the wrong metric at the model layer. A lab’s run-rate measures how much compute it is buying as well as how much value it is keeping. Treating model revenue like Nvidia revenue is a category error.
• The third implication is that the most durable profit pool may be less glamorous than the market assumes. NVIDIA’s moat is powerful but contestable: the cloud owners’ custom silicon is an attack from above. TSMC’s moat is harder to clone because it rests on physics, capital intensity, yield learning, and years of manufacturing execution. That does not make TSMC risk-free. It has Taiwan exposure, customer concentration, and cyclicality. But the moat itself is structurally harder to erode.
• The fourth implication concerns the capital now being committed. The major hyperscalers are reportedly tracking toward a combined 2026 AI capital spending approaching US$700 billion. That spending is ahead of demand. At the enterprise buyer level, an MIT study found that 95 per cent of organisations deploying generative AI had seen no measurable profit-and-loss impact. If the five per cent of successful deployments scale into the majority, the capital may be repaid. If not, much of the non-bottleneck stack remains a transfer mechanism feeding the gates.

The thesis can break in several ways. NVIDIA’s gate could erode faster than expected if custom silicon scales. A frontier lab could become a true toll collector if one model achieves a durable capability lead, or if regulation entrenches a small number of approved model owners. Enterprise demand could arrive faster than current evidence suggests. Or the binding constraint could migrate from chips to power, shifting the profit pool toward whoever controls dispatchable energy near data centres.

There is also a circularity risk. Some AI demand is financed by the same companies that benefit from it, through equity stakes, cloud commitments, reseller structures, and compute deals. That does not make Nvidia’s or TSMC’s profits fake. Their margins are real. But it does mean some of the revenue feeding those margins may be more fragile than organic demand would be.

The investor question is not whether AI is profitable. It plainly is, at the gates. The question is whether the demand behind those gates is durable enough, and arrives quickly enough, to repay everyone standing in line behind them.

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MoneyHero Group wants you to focus on the bright spots. Revenue climbed 15 per cent. Its shiny Wealth vertical surged 53 per cent. Its AI transformation story is compelling. And its Adjusted EBITDA loss? Down a whopping 68 per cent year-over-year to US$1.1 million.

On the surface, the Singapore- and Hong Kong-based personal finance comparison platform appears to be a company turning a corner.

Also Read: MoneyHero’s ‘turnaround’ built on a shrinking user base and retreat from SEA

Dig past the press release language, and the picture is considerably more complicated.

The net loss nobody wants to talk about

For the three months ended 31 March 2026, MoneyHero posted a net loss of US$6.7 million, nearly three times the US$2.4 million loss it recorded in the same period last year. That is not a rounding error. That is a 175 per cent deterioration at the bottom line.

The company’s explanation? Non-cash accounting items: a US$1.1 million swing in the fair value of warrant liabilities and US$2.4 million in unrealised foreign exchange losses from regional currency weakness against the US dollar. Fair enough, these are real accounting adjustments. But even if you strip them out entirely, the residual loss still lands around US$3.2 million, worse than the prior year’s US$2.4 million net loss. The narrative that operational performance is “robust” requires significant suspension of disbelief.

The press release buries this detail in a single paragraph, quickly pivoting to the Adjusted EBITDA figure, a non-IFRS metric that requires stripping out no fewer than six categories of charges to reach that headline-friendly US$(1.1) million number.

The mystery of US$1.6M in legal fees

Perhaps the most glaring anomaly in the report is a line item that receives precisely zero words of explanation in the management commentary: US$1.596 million in “non-recurring legal and professional fees and other expenses” incurred during the quarter.

In the same period last year, this figure was US$0.

The company categorises this as a non-recurring item and strips it out of Adjusted EBITDA. But US$1.6 million in legal costs is not a footnote; it is 9.7 per cent of the quarter’s total revenue. What litigation, regulatory matter, or professional engagement generated this bill? The report does not say.

We have reached out to the company for details and we will update this piece with the details as and when we hear from them.

This is likely a significant contributor to the 60 per cent spike in general, administrative and other operating expenses, which ballooned from US$2.19 million to US$3.51 million year-over-year, another figure conspicuously absent from the management commentary.

Revenue grew, but gross margins compressed

MoneyHero’s revenue of US$16.5 million is real and commendable. Double-digit growth across its core verticals — Credit Cards up 10 per cent, Personal Loans and Mortgages up 13 per cent, Wealth up 53 per cent, Insurance up 12 per cent — tells a story of genuine commercial momentum, particularly in Hong Kong, which surged 33 per cent to US$8.5 million and now accounts for 51.3 per cent of total revenue.

Also Read: MoneyHero swings to profit, but only on cost cuts and FX gains

But here is what the report does not highlight: the cost of revenue grew at 23.6 per cent, significantly faster than the 15 per cent revenue increase, rising from US$6.4 million to US$7.9 million. Gross margins are quietly compressing.

The company instead draws attention to the combined decline in technology costs, employee benefits, and advertising and marketing expenses, which came down 13 per cent year-over-year to US$8.5 million. This is a legitimate operational achievement, but the framing deliberately excludes the cost of revenue, which is by far the largest single cost line. It is a selective presentation designed to emphasise efficiency while obscuring margin erosion.

The “strategic retreat” in Southeast Asia

MoneyHero’s two smaller markets (the Philippines and Taiwan) posted revenue declines of 17 per cent and 12 per cent, respectively. The company frames these as deliberate strategic decisions: optimising margins, cutting low-quality volume, and building structural leverage. Perhaps. But consider this: the Philippines is home to 6.9 million of MoneyHero’s 9.8 million registered members (70 per cent of its entire user base), yet it contributed just US$1.47 million, or 8.9 per cent, of total revenue in the quarter. A market representing seven-in-ten of the group’s members is generating less than a tenth of its revenue. That is not a margin quality story. That is a monetisation failure, and calling it “strategic” is cold comfort for investors watching Southeast Asia’s largest member base sit largely idle.

Meanwhile, the platform’s overall traffic footprint shrank dramatically: monthly unique users fell 31 per cent year-over-year from 5.7 million to 3.9 million, and total sessions dropped 29 per cent from 17.5 million to 12.4 million. Clicks fell 33 per cent. The company is converting a smaller, higher-intent audience more efficiently (that part is true and defensible), but the scale of audience attrition is a genuine long-term risk that the report effectively sidelines.

Cash burn and the runway question

MoneyHero ended the quarter with US$27.984 million in cash, down from US$31.185 million at the end of 2025. That is a US$3.2 million cash burn in a single quarter. The company describes its balance sheet as “healthy” and highlights its debt-free status, which is accurate. But net current assets also declined, from US$37.5 million to US$32.8 million over the same period.

At the current burn rate, the company has roughly eight to nine quarters of runway, about two years. That is not a crisis, but it is not the picture of financial comfort the press release implies. The clock is ticking toward the “sustainable Adjusted EBITDA profitability” the company keeps promising.

The Adjusted EBITDA problem

“Our Adjusted EBITDA loss narrowed sharply by 68 per cent year-over-year,” said Danny Leung, Interim Chief Executive Officer and Chief Financial Officer, in the company’s earnings statement.

That figure is technically accurate. But to get from a US$6.7 million net loss to a US$1.1 million Adjusted EBITDA loss, the company removes US$5.4 million worth of charges — unrealised FX losses, warrant fair value changes, share-based payments, legal fees, depreciation, and interest. The adjustments are five times larger than the resulting metric. When a non-IFRS measure requires stripping out more than 80 per cent of the underlying loss to produce a headline number, investors should treat it as a directional indicator at best, not a proxy for cash profitability.

What is genuinely promising

None of this is to say MoneyHero is without real momentum. Its approval rate expansion, from 36 per cent to 48 per cent, is a meaningful operational achievement, particularly as total approved applications held flat at 156,000 despite a significant reduction in total applications. The Wealth vertical’s 53 per cent growth and the broader shift toward higher-margin products are structurally sound strategies. Hong Kong’s dominance is real. The AI-driven cost reduction story, though early, has tangible evidence in the declining technology and headcount costs.

Also Read: Decoding MoneyHero’s Q1: The profit push amid shrinking revenues

The question is whether the company can translate these genuine operational improvements into actual IFRS profitability and do so before its cash reserves force a capital raise or a more dramatic restructuring.

For now, MoneyHero is a company with a strong narrative, a compelling direction, and some numbers it would rather you did not look at too closely.

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Southeast Asia’s semiconductor story is no longer limited to assembly, testing and outsourced manufacturing. This list points to a region, led largely by Singapore and Malaysia, that is building more of the stack itself: custom ASICs, silicon IP, chiplet packaging, photonics, test equipment and fab services.

Some of these startups are tackling narrow but essential problems, such as radiation-hardened chips, RF test components and FPGA design software. Others are pushing local industry further upstream into design and advanced packaging.

Also Read: The quiet layer keeping the chip boom alive

Taken together, they suggest a semiconductor ecosystem that is becoming more specialised, more technical and less reliant on being the backend of someone else’s supply chain.

GreatAsic Technology

FusionAP

Silicon Box

Zero-Error Systems (ZES)

SkyeChip

Global TechSolutions (GTS)

Swift Bridge Technologies

Infinecs Systems

MaiStorage

Oppstar

LightSpeed Photonics

Core Semiconductor

Cloptech

Plunify

Divergent Technologies

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A few years ago, the logic of investing in AI seemed simpler: find the right model or product and bet on it. Today, models that take a year to build become obsolete in months. Betting on a specific AI product is like picking a favourite app at a moment when the operating system itself is changing.

If the bet isn’t on the product, then what? The real race is happening at the infrastructure level. Whoever controls the chips, the memory, the manufacturing equipment — controls the future of AI.

This is where the conversation begins about how and why Japan and Ukraine have ended up in a strategically important position but on opposite sides of the same stake. And why the partnership between these two countries is about structural logic and mutual reinforcement.

Japan’s quiet comeback

Japan is deliberately reclaiming its status as a global centre of semiconductor manufacturing with characteristic precision, backed by real results. According to the Brookings Institution, Japanese companies control 88 per cent of the global market for semiconductor coater/developers, 53 per cent of silicon wafers, and 50 per cent of photoresist — a step without which no chip can be manufactured. The government has committed over ¥10 trillion (US$65 billion) to AI and semiconductors by 2030. For private investors, this means the state has already absorbed a significant portion of the infrastructure risk.

At the same time, Japan is candid about its challenges. Over 70 per cent of Japanese organisations report a shortage of technical talent — 23 percentage points above the global average. Only around 30 per cent of Japanese companies report measurable results from digital transformation, compared to 80 per cent in the US and Germany.

Japan is building a powerful foundation. But infrastructure without engineering talent to deploy it has limits.

Pressure-tested innovation

Ukraine is the other side of this equation. Over the past two years, the number of Ukrainian AI specialists has grown by 17 per cent, reaching 6,100 people. The deepest expertise is concentrated in NLP and computer vision. The total IT workforce stands at approximately 300,000. In 2025, Ukrainian IT exports reached US$6.66 billion — making IT the second-largest export sector. About 20 per cent of Fortune 500 companies have dedicated development teams in Ukraine. This is not a niche market. It is a scale, proven in practice.

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

In parallel, a transformation has taken place that rarely gets noticed from the outside. Ukraine has become one of the leaders in digital governance. In the 2024 UN E-Government Survey, the country ranked 30th out of 193 states on the E-Government Development Index, and first in the world on the E-Participation Index.

An entire ecosystem has emerged around digital self-governance. Diia serves over 24 million users across 240 services. Diia.AI became the world’s first national AI assistant for public services. Diia.City, a legal framework that has attracted over 4,000 technology companies, 10 of which are unicorns.

Ukraine’s IT sector had been growing for years. But after 2022, the pace changed — not because of investment or market conditions, but because the stakes did. Engineers learned to build without a margin for error, with redundancy baked into everything, because failure had real consequences. That kind of pressure doesn’t just accelerate development. It changes what gets built and how.

This path was shaped under pressure, which is precisely why the solutions it produced have already passed the test of reality.

The case for systemic partnership

In AI, these two markets occupy different layers. Japan operates at the hardware layer (chips, robotics, industrial AI), Ukraine at the engineering layer (NLP, computer vision, GovTech architecture). Combining them closes a structural gap that neither country can close alone.

The zones where this fit holds are already visible. The most immediate is semiconductors and physical AI. Japan’s manufacturing precision meets Ukrainian software and algorithm engineering.

Also Read: To become better at prompt engineering, learn how to think like a manager

A natural next layer is robotics. Japan produces 38 per cent of the world’s industrial robots, and Ukraine has engineers who have built and deployed autonomous systems and tested them in difficult real-life conditions.

Joint R&D is another. Ukrainian teams are already embedded in Japanese industrial projects, but this is still point cooperation, not a systemic research pipeline.

The same logic applies to talent development — shared programs, structured internships, and long-term contracts that build pipelines rather than one-off engagements. And at the product level, both countries have something the other needs.

Japan has the hardware and the market access, Ukraine has the speed and the engineering culture to build globally scalable AI products.

On April 8 in Tokyo, we, as AI House with support from Roosh Investment Group, convened a panel discussion. The panel brought together government officials, business leaders, and researchers from both countries to examine a question: what Ukraine’s experience building a digital ecosystem under pressure actually looks like in practice, and where it connects with Japan’s own trajectory.

Such meetings are important not for what happens during them, but for what remains after — a shared understanding of where there is something real to build. That requires not one-off collaboration, but systemic engagement as long-term contracts, joint education programs, and structured exchanges.

The institutional groundwork is already in place. In 2023, Ukraine’s Ministry of Digital Transformation and Japan’s Digital Agency signed a memorandum on digital cooperation — covering cybersecurity, e-government exchange, and digital infrastructure. Yet the areas where both sides have something to offer go well beyond what that memorandum covers.

The trajectory of the AI economy is becoming clearer. More models mean more code, more compute, more chips, and more engineers. Demand at both layers will only grow. This partnership is about investing in people and systems.

As an investor, I look for structural logic — not just opportunity. Japan brings precision, depth, and the physical infrastructure of AI. Ukraine brings speed, adaptability, and engineers who learned to build without a margin for error. Individually, these are powerful. Together, they close a gap that neither country can close alone. That is the alignment I rarely see. Japan and Ukraine are exactly that case.

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Three months ago, I reviewed a case that looked like routine onboarding fraud — until none of the patterns I expected to find were there.

The application was for a mid-sized supplier with a decent credit profile, clean documents, and a sensible business model. The verification photos checked out. The voice call with the principal sounded normal. The contract was signed. Two weeks later, the bank account had gone dark, and the customer who had introduced them no longer recognised the name.

The application was synthetic. The photos were generated. The voice on the call was cloned. The business model existed only in the pitch deck.

I have spent fifteen years inside Indonesian risk functions — banking, insurance, sharia microfinance — and I have lectured on fraud detection in two of those years. The patterns I learned to look for, the patterns I taught others to look for, are not the patterns showing up in the casework now. The playbook I trusted for a decade has stopped working — faster than most risk teams in ASEAN are willing to admit.

What changed

Three patterns are new enough that they deserve to be named in the open.

Synthetic identity at scale. Until about eighteen months ago, identity fraud was bottlenecked by the cost of fabrication. A reasonable fake ID, a plausible address, a working phone, a consistent social presence — each piece required real effort. Generative AI has collapsed that cost curve. A single attacker can now generate hundreds of internally-consistent identities in an afternoon, each passing every check designed before 2024.

Voice and video impersonation. The “CEO email scam” of 2018 has evolved. The 2026 version is a thirty-second voice call from a number resembling your CEO’s, with the CEO’s actual voice asking for an urgent wire transfer. The voice is generated from three minutes of public conference recordings. The verification protocols banks trained employees on five years ago do not catch this attack.

Slow-burn synthetic onboarding. The most expensive new pattern is the patient one. An attacker creates a synthetic business identity, lets it operate for six to twelve months building a transaction history, applies for credit on the back of that history, draws down the credit, and disappears. The fraud is only visible in aggregate — after the loss is locked in.

Also Read: The AI economy is moving faster than our institutions

Why the old playbook fails

Most fraud playbooks across the region were built on three assumptions that no longer hold.

Fabrication is expensive. Identity verification, document checks, and onboarding interviews all assumed the cost of producing convincing fake material was high enough to deter scale. That assumption is gone. The marginal cost of one more fake identity is indistinguishable from zero.

Human verification is the gold standard. The voice call, the video interview, the in-person meeting — these were the fallbacks when automated checks were ambiguous. Each is now itself vulnerable to generated content.

Fraud is an event. The traditional playbook treats fraud as a moment — a fake invoice, a suspicious transaction, a flagged login. The 2026 pattern is increasingly a campaign — a multi-month sequence of legitimate-looking actions designed to build trust before the loss. By the time the loss arrives, the institution has already paid its onboarding cost on the relationship.

What is starting to work

Three responses are emerging.

Cross-channel correlation. Risk teams that connect onboarding, transaction monitoring, and customer service data into a single view are catching slow-burn fraud earlier. The signal is rarely visible inside one channel. It is almost always visible across three.

Liveness and behavioural verification. Identity checks that include real-time, randomised prompts — actions an attacker cannot pre-render — are catching synthetic identities at the door. Deployment across the region is uneven, but the institutions doing it well are seeing the difference in their loss numbers.

Internal red-teaming. The teams catching the most generated content are the ones running their own attacks against their own defences. That detection muscle is the closest thing to a real defence we have.

Also Read: AI governance in banking operations and decisioning

What needs to happen

The next eighteen months will be the most expensive in ASEAN fraud history for the institutions that have not retired the old playbook. Three moves would meaningfully shorten the gap.

Retire the verification protocols built for pre-2024 fabrication costs. They were designed for a world that no longer exists.

Invest in cross-domain risk talent before the loss events force it. The people who can sit between fraud, identity, and data engineering are not being trained anywhere at scale.

Treat fraud as a campaign, not an event. Build the systems and the reviews to detect patterns across months, not transactions across minutes.

The macro stakes

ASEAN’s financial system has digitised rapidly over the last five years. The fraud surface has digitised faster. The institutions that will absorb the next wave of losses are not the ones with the smallest fraud teams — they are the ones whose fraud teams are still working from the playbook that taught them to expect events instead of campaigns, individuals instead of synthetics, and effort-bottlenecked attacks instead of zero-marginal-cost ones.

The new playbook exists. The question is how quickly the institutions reading the old one will admit they are.

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MoneyHero’s Q1 2026 results tell two stories at once. The first is a genuine turnaround: revenue up, user growth holding, and a business that has quietly rebuilt its fundamentals after a bruising post-SPAC period. The second is harder to spin away, a US$6.7M liability sitting on the balance sheet like an uninvited guest at a celebration.

The legal overhang matters not just for the number itself, but for what it signals to investors still calibrating their trust in the company. MoneyHero went public on Nasdaq in 2023 via a SPAC merger, a route that attracted scepticism from the start. It has since worked hard to demonstrate it deserved a public listing. One strong quarter does not erase that history, but it contributes to the case.

The real question is whether management can resolve the liability cleanly and quickly, or whether it drags through subsequent quarters, diluting the narrative every time results are announced.

Southeast Asia’s financial comparison space is underleveraged relative to its population. MoneyHero has the infrastructure, the brand, and now, tentatively, the momentum. The US$6.7M problem is not fatal. But left unaddressed, it becomes the story, and that is a distraction the company cannot afford.

REGIONAL

MoneyHero’s winning quarter has a US$6.7M problem: The Nasdaq-listed financial comparison platform posted strong Q1 metrics, but a US$6.7M liability tied to a legacy legal dispute casts a shadow over its recovery narrative.

Airwallex raises US$320M Series H, valued at US$11B: The Sydney-founded, Asia-focused fintech secures one of the largest fintech rounds of 2026, lifting its valuation from US$6.2B. Airwallex operates across SEA and counts Singapore among its key markets.

Lazada cuts 5% of workforce in SEA market review: The Alibaba-owned e-commerce platform is trimming headcount across Southeast Asia amid an ongoing strategic review, as it faces mounting pressure from Shopee and TikTok Shop.

Swedfund backs Navegar fund to support Philippine SMEs: Swedish development finance institution Swedfund has invested in Navegar, a Philippines-focused private equity fund targeting job creation and business growth among small and mid-sized enterprises.

Carro said to explore US IPO amid growth push: SoftBank-backed used-car marketplace Carro is reportedly considering a US listing, a move that would mark one of SEA’s most significant public market entries in recent years.

Vietnam eyes Israel’s Yozma model for US$100M VC fund: Hanoi is studying the Yozma programme that seeded Israel’s startup ecosystem in the 1990s, as it designs a state-backed venture fund to catalyse domestic innovation.

Bukalapak accelerates across retail, gaming, and investment: The Indonesian platform is doubling down on its Mitra network and expanding into gaming and investment verticals, pivoting firmly away from its original e-commerce core.

AMC Robotics to build US$3.5M robotic dog factory in Vietnam: The firm will manufacture robotic dogs in Vietnam, targeting industrial inspection and security use cases across Asia. The move underlines Vietnam’s growing role in hardware manufacturing.

H3 Zoom raises US$3.6M to expand AI inspection tech in SEA: The startup’s computer vision platform, initially built for façade and railway inspection, is scaling across Southeast Asia and Japan after closing a pre-Series A round.

Echelon Philippines: profitability over growth at all costs: Founders and investors at Echelon Philippines 2025 debated the “unicroach” model, building lean, profitable businesses, as an alternative to the capital-intensive unicorn playbook.

Singapore sets sights on becoming global AI solutions hub: The government outlined an AI-empowered economy strategy aimed at attracting global AI deployments, upskilling workers, and positioning Singapore as a testbed for enterprise AI adoption.

Philippine AI startups step out of the shadows: A roundup of 15 Philippine AI startups, spanning healthtech, legal, and logistics, signals a maturing local AI scene that is beginning to attract regional investor attention.

15 SEA semiconductor startups moving beyond assembly: A new cohort of Southeast Asian semiconductor firms is moving into chip design, packaging, and materials — shifting the region’s role from contract manufacturer to technology developer.

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INTERVIEWS & FEATURES

15 SEA semiconductor startups moving beyond assembly: A new cohort of Southeast Asian semiconductor firms is moving into chip design, packaging, and materials — shifting the region’s role from contract manufacturer to technology developer.

WhatsApp’s new CEO is the headline; India’s data is the story: Meta’s appointment of a new WhatsApp chief matters less than the data governance questions it surfaces, particularly around India’s 500 million users and the country’s evolving data protection framework.

Can Ukraine’s engineers solve Japan’s tech talent gap: As Japan battles a chronic engineering shortage, a growing number of Ukrainian tech workers are finding roles with Japanese firms, raising questions about remote talent as a structural fix.

AI literacy in Thailand bypasses informal workers: Upskilling programmes are reaching office workers but missing the 63% of Thais in informal employment, a gap that risks deepening inequality as AI reshapes labour markets.

Can World ID solve the internet’s fake human problem: Worldcoin’s identity verification protocol is gaining traction as AI-generated bots flood digital platforms, but critics question its biometric data collection model and governance.

Smilegate hits US$40M initial close on new AI fund: South Korean gaming giant Smilegate has reached the first close of its AI-focused fund, targeting investments in AI infrastructure and applications across Asia.

Tribe Academy bets on AI bilingualism for SEA workers: The Singapore-based edtech is building AI bilingualism curricula, training professionals to work fluidly across English and local languages in AI-assisted workflows.

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INTERNATIONAL

Amazon commits fresh US$13B to AI infrastructure in India: The investment underlines India’s emergence as a priority market for hyperscaler AI build-out, with cloud, data centres, and localised model development all in scope.

Former Infosys chief launches startup to disrupt IT services: Ex-Infosys CEO Vishal Sikka’s new venture targets the legacy IT services model, aiming to replace headcount-driven delivery with AI-native software, a direct challenge to India’s outsourcing giants.

Flipkart expands quick commerce as Amazon ramps up in India: Walmart-backed Flipkart is broadening its rapid delivery push to counter Amazon’s India offensive, intensifying a battle that mirrors the SEA rivalry between Shopee and Lazada.

Trump administration bars Polestar from US EV market: The White House has blocked the Swedish-Chinese EV maker from selling its latest models in the US, citing national security concerns, a move with implications for Chinese-linked automakers eyeing SEA expansion.

Deepseek plans to double headcount across all departments: The Chinese AI lab, which rattled global markets earlier this year, is aggressively hiring across research, engineering, and product, signalling ambitions well beyond its current model portfolio.

AI was supposed to kill engineering jobs; new data says otherwise: Fresh labour market data suggests engineering roles are among the most resilient to AI displacement, challenging the dominant narrative around white-collar automation.

Notion Mail shuts down amid agent takeover: Notion has discontinued its standalone email client less than a year after launch, pivoting resources toward AI agent features, a sign of shifting product priorities across productivity tools.

Zuckerberg wants Meta to launch a prediction market: Meta’s CEO is pushing internally for a prediction market product, potentially placing Meta in direct competition with platforms like Polymarket and Kalshi.

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CYBERSECURITY

Synthetic identities cost nothing to make; ASEAN banks lag: Generative AI has collapsed the cost of creating synthetic identities to near zero, exposing critical gaps in ASEAN’s banking sector KYC and fraud detection infrastructure.

Polymarket hackers steal user funds: Decentralised prediction market Polymarket confirmed that attackers drained user funds in a security breach, raising fresh questions about the security of on-chain financial platforms popular with retail crypto users.

Anthropic accuses Alibaba of illicitly accessing its AI models: The US AI lab has filed accusations against Alibaba for allegedly circumventing access controls to extract model outputs, a case that could set precedents for AI model misappropriation in Asia.

Institutional rebalancing leaves crypto investors exposed: As large funds rotate out of crypto positions, retail investors in SEA face heightened volatility risk, particularly in markets where crypto is a primary savings vehicle.

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SEMICONDUCTOR

OpenAI unveils first custom chip built with Broadcom: OpenAI has taped out its debut in-house chip in partnership with Broadcom, a move that could reduce its dependence on Nvidia and reshape the AI silicon market.

Memory chip crunch pays off for US firm: Tight supply in the high-bandwidth memory market is boosting margins for a US chipmaker, underscoring the strategic value of memory in the AI compute stack, and the vulnerability of SEA firms reliant on imported supply.

Europe pushes back on Washington’s chip export controls: EU policymakers are challenging US semiconductor restrictions that they say disadvantage European firms, as the transatlantic chip war creates new fault lines in the global tech supply chain.

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AI

White House asks OpenAI to delay new model over safety fears: The Trump administration has urged OpenAI to slow-roll its next model release, marking an unusual instance of government intervention in frontier AI deployment timelines.

Anthropic’s Claude gains ground on ChatGPT among paid users: New data shows Claude is eroding ChatGPT’s dominance in the paid consumer segment, with users citing output quality and reliability as key switching factors.

Ex-Databricks AI chief targets 1,000x cut in AI power bills: A new venture founded by Databricks’ former chief AI officer claims it can slash AI inference energy consumption by three orders of magnitude, a claim with major implications for data centre planning across SEA.

AI agents will reshape customer journeys in SEA: Operators and brands across the region are deploying AI agents to handle end-to-end customer interactions, compressing sales cycles and reducing service costs.

Is the AI industry profitable? Yes, just not where you think: Revenue from AI is concentrating in infrastructure, cloud providers, chipmakers, and data centre operators, rather than in AI-native application startups, challenging prevailing investor assumptions.

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THOUGHT LEADERSHIP

Tokenised assets are on-chain; the liquidity hasn’t followed: Despite billions in tokenised real-world assets now live on public blockchains, secondary market liquidity remains thin, limiting the practical utility of tokenisation for institutional and retail participants alike.

From silicon to satoshis: tracing the global market unwind: A forensic look at how macro deleveraging ripples from traditional equity markets into crypto, with particular attention to the contagion pathways relevant to SEA retail investors.

How to build a board paper that answers the right question: A practical framework for structuring board papers around the core decision at stake, rather than burying it in context, aimed at founders preparing for board meetings.

Storytelling is now an analytical output, not a soft skill: The ability to construct a clear narrative from data has become a core professional competency, particularly for operators navigating investor and board communication in uncertain markets.

People don’t want productivity hacks; they want sustainable work: A pushback against hustle-culture optimisation frameworks, arguing that founders and operators are increasingly prioritising long-term wellbeing over short-term output maximisation.

Who am I in the age of AI? Identity, displacement, and awakening: A philosophical examination of identity in an era of AI-generated content and synthetic personas, asking what remains distinctly human in professional and creative work.

The post Ecosystem Roundup: MoneyHero’s turnaround story has an uninvited co-author appeared first on e27.

The digital asset landscape is currently grappling with a severe wave of selling pressure that has pushed major cryptocurrencies to multi-month lows. This downturn stems from a combination of deteriorating macroeconomic conditions, heavy institutional redemptions, and an intense cascade of derivatives liquidations.

Bitcoin has dropped 2.01 per cent over the past 24 hours to trade at US$59,782.21, closely tracking a broader market contraction of 1.85 per cent. Ethereum has suffered an even steeper decline, falling 3.46 per cent to US$1,567.16 and underperforming the market leader. Together, these movements have pulled the total cryptocurrency market capitalisation down by 1.76 per cent to a yearly low of US$2.06T.

The primary driver behind this market-wide reset is a sharp shift in macroeconomic sentiment, highlighted by an unexpected U.S. Personal Consumption Expenditures inflation reading. The inflation metric printed at 4.1 per cent, marking a three-year high. This hotter-than-expected data has reignited fears of a prolonged period of restrictive monetary policy, with market participants quickly increasing bets on further Federal Reserve interest rate hikes. Because the cryptocurrency market shares an 88 per cent correlation with the S&P 500 index, digital assets are behaving as highly sensitive risk assets within a tightening global liquidity environment.

This macroeconomic pressure quickly translated into physical selling across institutional channels. U.S. spot Bitcoin exchange-traded funds recorded their largest single-day redemption since early June, with investors pulling US$469.08M from these products on Wednesday, June 24. This massive exit represents the fifth consecutive day of net institutional outflows, led primarily by BlackRock and its spot product, which accounted for a significant portion of the capital flight.

Also Read: How institutional rebalancing leaves crypto investors vulnerable

At the same time, spot Ethereum funds experienced US$30.24M in net redemptions on the same day. This persistent drain on institutional liquidity has removed a critical layer of price support and created automated selling pressure as fund managers liquidate their underlying digital holdings to meet redemption demands.

Beyond institutional product outflows, the spot market faced unexpected structural headwinds from major trading platforms. Rumours and reports began circulating across social networks that prominent global exchanges, specifically Binance and Coinbase, were actively offloading large quantities of Bitcoin. This potential institutional distribution added to an already significant supply overhang, shaking retail investor confidence and accelerating the downward price action.

As spot prices cracked, the decline triggered a violent mechanical unwinding in the derivatives market, which drastically amplified the velocity of the sell-off. Over a 24-hour window, forced liquidations across the entire cryptocurrency market surpassed US$1B. Leveraged positions linked directly to Bitcoin accounted for US$428.87M of this total, with long positions making up US$337M of the wiped-out contracts. Over the final 12 hours of the crash, short positions accounted for 63 per cent of the immediate liquidations.

Meanwhile, over-leveraged traders in the Ethereum market saw US$230M in contracts forcibly closed. Despite this massive purge of speculative bets, average funding rates remarkably managed to stay positive, confirming that many market participants were caught off guard in heavily leveraged long positions.

This severe deleveraging event has pushed technical indicators into deep underbought territory. Ethereum broke decisively below both its seven-day simple moving average of US$1,675.94 and its 30-day simple moving average of US$1,760.28. Its Relative Strength Index has dropped to 30.5, which confirms deeply oversold conditions but offers no immediate structural support. For the broader market, the overall capitalisation sits precariously at its US$2.06T baseline, with a global Relative Strength Index of 35.89 suggesting that while the market is stretched to the downside, a definitive bullish reversal has not yet commenced.

Looking ahead, the immediate trend for the digital asset space remains distinctly bearish, though the market is rapidly approaching a massive technical inflection point.

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The post Why the 4.1% PCE inflation print just turned crypto into a high beta risk asset appeared first on e27.

At Echelon Philippines 2025, a dynamic fireside chat brought together a new wave of Filipino founders shaping the startup landscape with purpose and boldness.

Moderated by Ritch Traballo of NextHire, the session featured Alyssa Wee of Danny PH, Princess Ventures of BuddyBetes, Orange Silverio of Tambanokano Aqua Farm, and AC Alyzsa Dy of Villigro Philippines.

Each founder shared insights drawn from building mission-driven ventures across diverse industries, from health tech and agribusiness to social enterprise. Their stories highlighted how the next generation of Filipino entrepreneurs is not just chasing growth, but creating meaningful impact in their communities and beyond.

The post Echelon Philippines 2025 – Lessons from the next generation: How today’s emerging founders are building bold, purposeful startups appeared first on e27.

Airwallex has raised US$320 million in a Series H round, lifting its valuation to US$11 billion as the fintech company doubles down on AI-driven financial software, cross-border payments infrastructure, and regulatory expansion.

The round was led by returning investor Addition, with participation from Baillie Gifford, Hummingbird, QED Investors, T. Rowe Price, Hedosophia, Haun Ventures, Washington University in St. Louis, and Amex Ventures.

The new valuation marks a rise from US$8 billion in December 2025, reflecting investor confidence in Airwallex’s ability to move beyond payments and into a broader financial operating system for businesses.

Also Read: Why Airwallex chose acquisition over patience in Korea

The company said the capital will be used to accelerate product development in autonomous finance and agentic commerce, expand infrastructure and licensing coverage in new markets, and grow teams building AI-native financial software.

Founded in Melbourne in 2015 and now co-headquartered in San Francisco and Singapore, Airwallex has become one of the most prominent fintech infrastructure players with roots in Asia Pacific. Its Singapore base is particularly important as the company looks to serve Southeast Asian businesses operating across multiple currencies, payment systems, and regulatory regimes.

For regional startups, e-commerce merchants, SaaS companies, and marketplaces, cross-border payments remain a major operational drag. Businesses expanding from Singapore into Indonesia, Vietnam, Thailand, Malaysia, or the Philippines often face fragmented banking relationships, FX costs, local compliance requirements, and settlement delays.

Airwallex is betting that its combination of licences, payment rails, treasury products, cards, and software can give such companies a more unified financial stack.

“We believe this is the most consequential moment in the history of global finance, and we are building accordingly,” said Jack Zhang, co-founder and CEO of Airwallex. He added that the company’s decade-long investment in licences, local network integrations, and settlement rails gives it the infrastructure needed for autonomous finance and agentic commerce.

From payments to AI finance software

The fundraising comes as Airwallex pushes deeper into software and automation, an area where global fintech firms are increasingly trying to defend margins and improve customer stickiness.

The company announced two product initiatives alongside the funding: T:0 and Airi.

T:0 is an AI-native finance platform designed to automate core finance functions for businesses, including bookkeeping, forecasting, tax, compliance, and reporting. Airwallex says the product is intended to give founders and finance teams “CFO-grade” books from day zero without requiring a later migration.

Also Read: Airwallex raises US$330M in Series G led by Addition to power US expansion

The product is currently in private beta and is expected to become more widely available in the coming weeks.

If successful, T:0 could place Airwallex in closer competition with accounting software providers, spend management platforms, and embedded finance players. For Southeast Asian startups, the appeal could be significant. Many early-stage companies in the region manage finance through a patchwork of spreadsheets, local accounting tools, bank portals, payment gateways, and external accountants. A more integrated platform could reduce complexity, though adoption will depend heavily on localisation, compliance coverage, and trust in AI-driven workflows.

Airi, the second product, is a consumer wallet infrastructure initiative aimed at agentic commerce. At launch, it will include Airwallex’s existing one-click checkout capability, which the company said delivered up to a 14 per cent increase in successful checkout conversions for digital merchants in early testing.

Over time, Airwallex plans to expand Airi into wallet infrastructure for delegated agent payments, spend limits, permission controls, and multi-currency balances. The idea is to support a future where AI agents may be able to make purchases or execute transactions on behalf of users within predefined rules.

That vision remains early, and agentic commerce is still more concept than mainstream behaviour. But fintech companies are increasingly positioning themselves for a world in which AI systems do not merely recommend purchases or financial actions but execute them. In that environment, the companies with regulated payment infrastructure, identity checks, spend controls, and merchant networks could hold an advantage.

Southeast Asia remains a strategic market

Airwallex’s growth trajectory is tied closely to the rise of borderless digital businesses, a trend especially visible in Southeast Asia.

The region’s startups and digital merchants often operate regionally from the outset, selling to customers, hiring teams, and paying suppliers across borders. Yet financial infrastructure has not always kept pace. Local payment preferences vary widely, card penetration differs by market, and SMEs often struggle to access efficient FX, treasury, and global payment tools.

Singapore has become a preferred base for fintech firms serving this demand because of its regulatory environment, financial services talent, and role as a regional headquarters hub. Airwallex’s decision to maintain a co-headquarters in Singapore reflects the city-state’s importance not just as a market but as a launchpad into Asia-Pacific.

The company says it now holds more than 85 licences across North America, Europe, the Middle East, and Asia Pacific. That regulatory footprint is central to its pitch: instead of simply layering software on top of third-party banking and payment partners, Airwallex has spent years building its own network integrations and compliance capabilities.

Lee Fixel of Addition said Airwallex has built infrastructure that is “unusually hard to replicate”, adding that AI will favour companies building on top of real financial infrastructure rather than around it.

Revenue and transaction volumes climb

Airwallex also disclosed fresh operating metrics. In March 2026, it reached US$1.3 billion in annualised revenue, up 74 per cent year-on-year. Annualised transaction volume reached US$287 billion, up more than 120 per cent year-on-year.

The company said more than 90 per cent of revenue now comes from customers using more than one Airwallex product, suggesting that it is succeeding in cross-selling beyond its initial payments use cases.

Airwallex serves more than 676,000 businesses globally, either directly or through platform customers. Its products include payment acceptance, billing, global accounts, corporate cards, and spend management.

The company employs more than 2,300 people across 27 offices.

The latest funding gives Airwallex more firepower at a time when competition in global business payments is intensifying. Stripe, Adyen, Wise, Payoneer, Revolut Business, and a growing number of regional fintechs are all chasing parts of the same opportunity.

Also Read: Fintech companies targeting the next billion users are living a pipe dream. Here’s why

For Southeast Asia, the key question is whether Airwallex can convert its global infrastructure into local advantage. The region is large, fragmented, and fast-growing, but it rewards companies that can navigate market-by-market complexity.

With fresh capital and a stronger AI software push, Airwallex is signalling that it wants to be more than a payments provider for regional businesses. It wants to become the operating layer for how they manage money globally.

The post Airwallex doubles down on agentic commerce with US$320M funding round appeared first on e27.

China-affiliated AMC Robotics Corporation will invest US$3.5 million to build out and equip a new manufacturing facility in Vietnam’s Bac Ninh province, marking its first major production foothold in Southeast Asia as the region’s factories and warehouses gradually move towards automation.

The company said it has signed a lease agreement for a 6,150-square-metre facility in Bac Ninh, an industrial province near Hanoi that has become one of Vietnam’s most important electronics and manufacturing clusters.

Also Read: VinRobotics takes humanoids to ICRA and COMPUTEX, signalling Vietnam’s rise in robotics

The investment will be used to prepare the plant for production, with the first phase focused on AMC Robotics’ NovaArm robotic arm. Initial production is expected to begin in the second half of 2026.

For AMC Robotics, the move shifts the company from product development towards manufacturing execution. For Vietnam, it adds another name to a growing list of technology and hardware companies using the country as a base to serve regional and global supply chains.

Vietnam as a Southeast Asia production base

AMC Robotics said Vietnam will serve as a long-term hub for its production and operations in Southeast Asia. That positioning is significant at a time when manufacturers are reassessing supply chains across Asia and looking for locations that offer proximity to China, competitive costs, improving infrastructure and access to regional markets.

Vietnam has been one of the main beneficiaries of the China-plus-one strategy, particularly in electronics, consumer devices and industrial components. Bac Ninh, in particular, has attracted global manufacturers because of its industrial parks, road links to Hanoi, and access to ports in northern Vietnam.

The robotics sector is still relatively early in Southeast Asia compared with China, Japan, South Korea, and the US. But demand is rising as warehouses, factories and logistics operators in the region face labour shortages, wage pressure, higher throughput requirements and the need for more predictable operations.

E-commerce, third-party logistics, electronics manufacturing, and automotive supply chains are among the sectors likely to drive automation adoption in Southeast Asia. Vietnam, Thailand, Malaysia, Indonesia, and Singapore have all seen growing interest in robotics and industrial automation, although adoption levels vary sharply by market and sector.

AMC Robotics’s decision to manufacture in Vietnam suggests the company sees the region not only as a production base, but potentially as an end market for warehouse and industrial automation systems.

NovaArm comes first

In the first phase, the Bac Ninh facility will focus on the production of NovaArm, AMC Robotics’s robotic arm designed for high-load, high-precision warehouse sorting and industrial automation applications.

The company has not disclosed the planned production capacity of the facility, the expected number of jobs to be created, or whether the Vietnam plant will serve regional customers, global exports, or both. It also did not specify how much of the manufacturing process will be handled locally versus assembled from imported components.

The absence of those details makes it difficult to assess the immediate economic impact of the investment. Still, the choice of Vietnam is noteworthy because robotics manufacturing requires a more advanced supplier and engineering base than traditional assembly operations.

Also Read: Clear Robotics raises US$1.75M to scale electric, self‑driving boats across South Asia, ASEAN

If AMC Robotics scales production successfully, the plant could become a test of Vietnam’s ability to move further up the manufacturing value chain, from electronics assembly into more complex automation hardware.

Sean Da, Chairman and CEO of AMC Robotics, said securing the facility is an important step as the company moves from product development to manufacturing. He said the plant would provide the infrastructure needed to support the launch of NovaArm and establish a scalable foundation for future products, including Kyro.

Kyro robotic dog in the pipeline

AMC Robotics plans to use the Vietnam facility as a base for future expansion, including the production of Kyro, its quadruped robotic dog.

Quadruped robots have attracted interest globally for use in industrial inspection, security, hazardous environment monitoring and research. However, commercial adoption remains uneven, partly because of high costs, limited use cases and the need to prove reliability in real operating environments.

For Southeast Asia, the market for such robots is likely to develop gradually. Industrial campuses, energy facilities, construction sites, ports and large manufacturing plants could be early adopters, but price sensitivity and after-sales support will be critical.

By placing future Kyro production in Vietnam, AMC Robotics is signalling that it wants the facility to support more than a single product line. The company appears to be laying the groundwork for a broader hardware manufacturing operation in the region, although its success will depend on execution, supply chain depth and customer demand.

China links, US office, Vietnam factory

AMC Robotics maintains its executive office in the US, but the company is led by Sean Da, a Chinese national best known as the founder of YI, the Chinese camera brand.

YI previously had a strategic partnership with Xiaomi and received investment from the Chinese technology company. Da has said YI is not owned by Xiaomi.

That background is crucial because robotics, like drones, semiconductors and connected devices, increasingly sits at the intersection of technology, manufacturing and geopolitics. Companies with Chinese links are facing more scrutiny in some Western markets, while at the same time looking to diversify production footprints outside mainland China.

Vietnam has emerged as a natural destination for such diversification. It offers geographic proximity to Chinese suppliers while providing companies with an alternative manufacturing base. For firms serving global customers, a Vietnam footprint can also help reduce concentration risk in China.

However, China-affiliated companies operating from Vietnam may still face questions from customers, regulators and partners about ownership, supply chains and data handling, especially in sectors involving autonomous systems and connected devices.

Also Read: “Data, not hardware, is the real bottleneck in humanoids”: Matrix Robotics CEO Allen Zhang

AMC Robotics has not disclosed whether its Vietnam plant will be used to serve the US market, Southeast Asia, or other regions. But its US executive presence, Chinese founder background and Vietnam manufacturing base reflect a broader shift in how hardware startups and robotics companies are structuring their operations in an increasingly fragmented global market.

Southeast Asia’s automation opportunity

For Southeast Asia, the AMC Robotics investment is modest in size but strategically relevant.

The region has spent years positioning itself as a manufacturing alternative to China. The next stage will be harder: attracting and retaining companies that bring higher-value production, engineering expertise and industrial technology capabilities.

Robotics manufacturing could contribute to that transition, but only if local ecosystems develop alongside foreign investment. This includes skilled technicians, precision component suppliers, software and systems integration talent, and customers willing to adopt automation at scale.

Singapore has been the region’s most advanced market for robotics deployment, particularly in logistics, healthcare and service automation. Vietnam, Malaysia, and Thailand have stronger manufacturing bases, while Indonesia and the Philippines offer large labour markets where automation adoption will be shaped by cost, productivity and policy considerations.

AMC Robotics’s Bac Ninh facility will not transform Southeast Asia’s robotics landscape on its own. But it adds to a pattern of hardware and automation companies treating the region as more than a low-cost assembly destination.

If NovaArm production begins as planned in 2026, the plant could become an early indicator of whether Vietnam can capture a larger share of the robotics manufacturing value chain, and whether Southeast Asia’s automation demand is strong enough to support the companies now setting up shop in the region.

The post AMC Robotics to build US$3.5M Vietnam factory as SEA automation race heats up appeared first on e27.