At Siam Paragon’s Speaker Lounge in Bangkok, against the backdrop of SITE 2026, Sophia Chong, Executive Director at the Hong Kong Trade Development Council (HKTDC), sat down to explain why Thailand, and Southeast Asia more broadly, has become central to Hong Kong’s global strategy.

The occasion was a fitting one: HKTDC had just received the Global Partnership Award from Thailand’s National Innovation Agency (NIA), marking eight years of collaboration since the two organisations signed their first memorandum of understanding in 2018.

Also Read: How Thailand’s NIA is driving global collaboration for Thai innovation

“We are very honoured and privileged, because the prime objective of visiting SITE is to receive this award, which exemplifies the longstanding partnership between NIA and HKTDC,” Chong said. “We started our MOU as early as 2018, and since then we have been organising mutual missions — Hong Kong startups to SITE and other Thai events, and NIA also bringing Thai startups to Hong Kong, to events such as InnoEX, as well as Food Expo, because Thailand is also very advanced in food tech.”

Bangkok’s growing role in a 51-office network

Thailand’s importance to HKTDC isn’t incidental; it’s structural. The council operates 51 offices worldwide, seven of them across ASEAN, with Bangkok serving as the regional hub overseeing Southeast Asia and South Asia, including India. That positioning reflects a broader shift in Hong Kong’s trade patterns since the pandemic.

“We observed a shift in the demographics as well as in the global trade scenario,” Chong explained. “ASEAN has become the second-largest export market for Hong Kong. From 2019 to 2025, we’ve witnessed our exports grow by more than 60 per cent, which is a very huge number.”

This growth has been reinforced by Hong Kong government policy. Under the GoGlobal Task Force — led by the Secretary for Commerce and Economic Development and delivered jointly by HKTDC, InvestHK and a number of professional bodies and service providers — eligible mainland Chinese companies are being actively guided into Southeast Asian markets, including Thailand, via Hong Kong. “So that’s why RCEP is becoming increasingly important,” Chong noted, referencing the Regional Comprehensive Economic Partnership. “For many companies, ASEAN has strategic proximity, and Hong Kong is a growing market with a growing role.”

Hong Kong’s case as a global springboard

Asked how Hong Kong differentiates itself from Singapore, the other major hub Southeast Asian founders often weigh, Chong pointed to the city’s “One Country, Two Systems” framework as its defining advantage. “Hong Kong has a unique advantage under one country, two systems, under the Chinese Mainland,” she said. “Because of our common law system, our free flow of capital, free flow of people and information, and our own currency — all of this, together with a strong intellectual property protection scheme, forms the foundation for doing business with the international community.”

That foundation, she added, works both ways: mainland Chinese companies use Hong Kong as a springboard to the world, while international firms use it to enter the Chinese Mainland with reduced risk. “Hong Kong service providers understand the culture, understand the system, understand how it works, so we provide a very good partnership before going into the market.”

Chong outlined three “drive engines” underpinning Hong Kong’s value proposition, echoed recently by the city’s Financial Secretary: its role as an international financial centre, its evolution as a sophisticated trade hub moving up the value chain into advanced manufacturing and branding, and its emerging status as an innovation and technology hub that commercialises research for global markets.

Also Read: Why the tech world is heading to Hong Kong in April 2026

The figures back up the financial claim. Hong Kong became the world’s top IPO fundraising centre last year, raising the equivalent of roughly US$37 billion across 119 new listings, with subsequent capital raises adding a further US$66 billion, pushing total capital raised past US$100 billion in a single year. “This really is a record,” Chong said.

Biotech, green finance and the Greater Bay Area advantage

For biotech and healthcare startups specifically, Hong Kong has introduced listing rules, Chapters 18A and 18C, allowing pre-revenue, pre-profit companies to raise capital provided they meet the criteria set by the Securities and Futures Commission and the Hong Kong Stock Exchange. “This has already facilitated hundreds of companies being listed and raising capital in Hong Kong in the healthcare and biotech space,” Chong noted.

Layered on top is access to the Guangdong-Hong Kong-Macao Greater Bay Area, home to some 87 million people across nine mainland cities plus Hong Kong and Macau. Through the Hong Kong and Macao Medicine and Equipment Connect introduced in 2021, drugs and medical devices approved in Hong Kong can be fast-tracked into 71 designated medical institutions across the Bay Area. “As of 30 April this year, we already have 71 drugs and 91 medical devices going into practice in the Greater Bay Area,” Chong said.

“Some of those drugs from the US, have since been approved by the National Medical Products Administration in Beijing to apply nationwide, so you can see, step by step, how Hong Kong leads into the Greater Bay Area and then into the mainland market of 1.4 billion people.”

Green finance is another pillar Chong highlighted as an underappreciated growth area. Hong Kong has arranged green and sustainable bonds for eight consecutive years, a first in Asia, with the government issuing roughly US$32 billion in green bonds since 2018 across more than 110 projects covering green buildings, waste management and resource recovery. Green startups in the city have grown 150 per cent over five years, now numbering around 265.

“We think that green startups and green compliance are the current high-growth area in the world,” she said.

From trade facilitator to startup accelerator

Beyond capital markets, Chong emphasised HKTDC’s evolving role as an ecosystem builder. Startups landing in Hong Kong become eligible for government funding schemes such as the Innovation and Technology Fund, administered by the Innovation and Technology Commission, and gain access to incubators including Hong Kong Science Park and Cyberport. InvestHK, meanwhile, handles the practical side of relocation, from company setup to finding schools for founders’ children.

“HKTDC will provide marketing and business-matching opportunities through our sectoral focus exhibitions,” Chong said, pointing to more than 40 world-class events spanning healthcare, logistics, electronics and lifestyle sectors, alongside international missions to CES in Las Vegas and Viva Technology in Paris.

Much of this activity is now anchored around Hong Kong’s Northern Metropolis, a new innovation corridor bordering Shenzhen. The Hong Kong Innovation and Technology Park has received a fresh government injection of roughly US$1.3 billion this year, on top of about US$2.2 billion previously committed, with two further parks — San Tin Technopole and Hung Shui Kiu — each drawing a similar US$1.3 billion investment to attract R&D, advanced manufacturing and production-focused technology firms.

Also Read: Why Hong Kong’s metro just became every marketer’s dream

“So HKTDC’s role is not just event organising, but rather business matching and deal-making,” Chong said. “We are moving up the value chain, apart from being the superconnector, we want to give value add, and ultimately be a super partner, so that startups can enter the market with reduced obstacles.”

Looking ahead

With Hong Kong’s trade with ASEAN growing nearly 64 per cent between 2019 and 2025, Chong sees the relationship deepening further over the next two years, particularly in biomedicine, green technology, robotics and the low-altitude economy. For Southeast Asian founders weighing where to scale next, her message was clear: Hong Kong isn’t just a financial centre, but a proven pathway into one of the world’s largest and fastest-growing markets.

The post Hong Kong’s pitch to SEA: “We want to be your super partner” appeared first on e27.

Robots are getting better at seeing, speaking, and planning. What they still struggle with is the messy business of doing.

For a machine to reliably pack a box, wipe a table, load a warehouse shelf or assist someone at home, it needs more than internet text and video clips. It needs to understand grip, weight, timing, movement, and context — the kind of physical judgement humans build through repeated experience. Singapore-based Ropedia is betting that this missing layer will become one of the most important infrastructure markets in artificial intelligence (AI).

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

The startup has raised US$22 million in pre-Series A funding, taking its total funding to US$30 million. The round was backed by venture investors focused on AI, deeptech and infrastructure in Southeast Asia, though the company did not disclose specific investor names.

A previous round included investors and super angels connected to Google, Andreessen Horowitz, NVIDIA, and Amazon.

Ropedia will use the capital to expand its real-world data collection operations into Southeast Asia and North America, grow its Singapore and US teams, and increase manufacturing of its wearable capture hardware. It also plans to strengthen its data platform with annotation tools, quality analytics, and compliance systems, while expanding research into data foundation models and world models, AI systems designed to build an internal understanding of how the physical world behaves.

Why physical AI needs different data

The surge of interest in “physical AI” follows the rapid advances made by large language models. The basic idea is to bring AI out of screens and into machines that can operate in the real world, from industrial robots and autonomous vehicles to humanoids and home assistants.

But robots face a harder data problem than chatbots. Text-based AI systems were trained on enormous volumes of written material already available online. Robotics data is scarcer, more expensive to collect, and far more dependent on context. A video of a person lifting an object may show the action, but not always the force used, the hand motion, the depth of the scene or the subtle adjustments made along the way.

That is where Ropedia is positioning itself. Its platform captures what the company calls multimodal human experience data: egocentric video, depth, motion, and audio collected through proprietary wearable hardware. The data is then synchronised, processed and converted into datasets that robotics and embodied AI developers can use to train their models.

Also Read: Rise of the machines: 20 robotics startups shaping Southeast Asia’s future

“A robot can’t play baseball by watching a video any more than you could learn to ride a bike by reading about it,” said Zhaoxi Chen, CEO and co-Founder of Ropedia. “The robot must understand what it’s like to grip a bat and know the timing it takes to hit a ball.”

That explanation gets to the heart of the challenge. The next stage of robotics is not just about recognition; it is about interaction. Machines need training data that records how humans move through kitchens, workshops, factories, offices and streets, and how those movements change across cultures, layouts and environments.

A Singapore base for a global robotics data play

Founded in the second half of 2025, Ropedia is headquartered in Singapore and also has an office in Mountain View, California. Its founding team combines academic research and industry experience in computer vision and embodied AI.

Chen’s work spans 3D computer vision, generative foundation models and multimodal content generation. CTO Fangzhou Hong previously worked on Meta’s egocentric multimodal intelligence research, while Chief Scientist Ziwei Liu is an Associate Professor at Nanyang Technological University in Singapore.

That Singapore connection matters. Southeast Asia is becoming a useful testbed for physical AI because of its mix of advanced manufacturing, logistics hubs, dense urban environments and service-heavy economies. Singapore, in particular, has pushed robotics in sectors such as healthcare, cleaning, logistics and food services, partly because of labour constraints and its high-cost operating environment.

The region also offers environmental diversity that robotics companies cannot easily replicate in a lab — humid warehouses, crowded retail spaces, mixed transport systems and varied household settings.

Also Read: dConstruct lands US$125M Series A to scale robotics for GPS-denied environments

For Ropedia, expanding data collection in Southeast Asia could help its customers train models that are less brittle when deployed outside controlled environments. A robot trained only on neatly staged factory or home data from one geography may fail when faced with different lighting, room layouts, tools, packaging, languages or user behaviour.

The company claims its approach can reduce data-collection costs by up to 50x compared with traditional methods. Its wearable device, called HOMIE, has entered mass production to support larger deployments. Ropedia says it already serves more than 20 robotics and foundation model companies across North America, China and Singapore, in areas including embodied AI and spatial intelligence.

Its flagship dataset, Xperience-10M, is described by the company as one of the world’s largest human experience datasets. Ropedia also offers custom Data-as-a-Service products for robotics and embodied AI developers that need task-specific or geography-specific data.

The competitive field

Ropedia is entering a market that is still forming, but not empty. Its competitors are likely to come from several directions. Data-labelling and AI infrastructure companies such as Scale AI, Appen, and TELUS International AI have long served machine learning teams, though much of their work has focused on labelling rather than capturing physical interaction data at source.

Synthetic data companies such as Datagen have targeted computer vision and simulation use cases, offering another way to train models when real-world data is scarce. Meanwhile, robotics and embodied AI startups such as Physical Intelligence, Skild AI, and Figure AI are building their own model and data pipelines, which could reduce their reliance on outside providers.

Ropedia’s bet is that independent, large-scale, real-world human experience data will become a shared infrastructure layer, much like cloud infrastructure did for software startups.

The question is whether robotics companies will buy that layer externally or continue building it in-house. In AI, the answer has often been mixed: companies outsource some infrastructure when it saves time, but keep strategically sensitive data close. Ropedia will have to prove not only that its datasets are cheaper and broader, but that they are reliable, compliant and meaningfully improve model performance.

That compliance layer may become more important as physical AI leaves research labs. Data captured in homes, factories, and public spaces can raise privacy and consent issues, especially when audio, video, and movement data are involved. Southeast Asia’s regulatory environment is fragmented, with different data protection rules across markets, so any company building regional data infrastructure will need careful governance from the start.

Also Read: Beyond productivity: How AI can make work more human

Still, the timing is favourable. Investors are hunting for the next infrastructure layer after the boom in large language models, while robotics companies are under pressure to show that their systems can move beyond demos. If Ropedia can turn human physical experience into structured, reusable training data, it could sit close to the picks-and-shovels layer of the robotics economy.

Chen frames the opportunity in infrastructure terms: cloud computing needed data centres, language AI needed internet text, and physical intelligence will need real-world interaction data. The claim is ambitious, but the direction of travel is clear. If AI is to move from answering questions to handling objects, opening doors and working beside people, it will need to learn from the physical world, not just look at it.

The post Ropedia raises US$22M to build the data layer for robots that understand the real world appeared first on e27.

If the reported OpenAI-Hugging Face cyber incident stands up under scrutiny, the most alarming part is not that an AI system found a way to cheat a test. It is that one of the world’s most powerful AI companies appears to have built the conditions for that failure, then rushed to describe the result as something close to autonomous misbehaviour.

That framing matters. A great deal.

According to the account so far, OpenAI’s models, operating with loosened safeguards inside a sandbox, allegedly escaped the testing environment, used stolen credentials, discovered a vulnerability, accessed Hugging Face’s systems and pulled secret information to game an evaluation. This is being described as unprecedented. Fair enough. But “unprecedented” should not become a euphemism for “nobody is accountable”.

Also Read: The future isn’t people or machine — It’s people with machine

The more useful way to read this episode is brutally simple: humans set the goal, humans relaxed the constraints, humans connected the system to a world full of targets, and humans are now tempted to speak as if the machine developed intentions of its own. That is not a technical nuance. It is the entire story.

Stop anthropomorphising the machine

Every time the industry says an AI system “went rogue”, it quietly shifts blame away from the people and organisations that designed, deployed and incentivised it.

Machines do not wake up with malice. They optimise against the environment and permissions given to them. If an AI model was told to pursue “complex attack paths”, then found a way to break out of a loosely controlled sandbox and target a third party, that is not evidence of machine agency in the moral sense. It is evidence of a badly bounded experiment.

This is where the AI industry remains maddeningly slippery. The same companies that insist their systems are not conscious are suddenly happy to imply a kind of machine cunning when something goes spectacularly wrong. It is a convenient trick: anthropomorphise the product, depersonalise responsibility.

For startup founders and builders across Southeast Asia, that should set off sirens. The region has spent the past decade learning, often the hard way, that “move fast and break things” is just Silicon Valley’s more stylish phrase for pushing risk downstream. If a frontier AI lab can normalise the idea that a breakout attack is an unfortunate by-product of innovation, smaller companies will absorb the lesson that messy collateral damage is acceptable so long as it happens in the name of capability.

It is not acceptable.

The sandbox excuse is not a defence

The industry also leans too heavily on the word “sandbox”, as if it were a magic ward against consequences.

A sandbox is only as secure as its boundaries, access controls and failure assumptions. In cybersecurity, there is no medal for saying the intrusion was meant to happen in a controlled environment when the obvious problem is that it did not stay there. That is like assuring the public a chemical spill happened in a lab, while the toxic sludge is already in the river.

And let us not pretend this is just a niche technical mishap inside a single company’s testing stack. AI labs are now building systems designed to write code, probe systems, automate workflows, search across tools and make multi-step decisions with minimal human oversight. In plain English: they are creating machines that can chain actions together in ways that look increasingly like operational autonomy, whether or not the machine “understands” what it is doing.

Also Read: AI human hybrid support: Why customers still prefer real conversations

That is exactly why governance cannot be bolted on after the demo.

We have seen this pattern before

The OpenAI episode would be disturbing enough as a standalone story. It is more troubling because it fits a broader pattern: powerful institutions deploying AI into sensitive domains first, then acting surprised when the harms are real, scalable and difficult to reverse.

The Middle East offers the starkest example. AI is not some hypothetical future risk in warfare; it is already entangled in present conflict. Project Nimbus, the US$1.2 billion cloud computing contract involving Google, Amazon, and the Israeli government, became a global flashpoint precisely because cloud and AI infrastructure do not exist in a moral vacuum.

Reporting has also drawn attention to AI-assisted targeting systems, such as Lavender and Gospel in Israel’s war in Gaza. Whatever one’s politics, the core point is unavoidable: AI systems are already being embedded in kill chains, surveillance architectures and state power.

Governments elsewhere have misused algorithmic systems in less visibly violent but still deeply damaging ways. In the Netherlands, automated risk tools played a notorious role in the childcare benefits scandal, where thousands of families were wrongly accused of fraud.

In the UK, the Home Office’s visa streaming algorithm was scrapped after criticism that it baked nationality-based discrimination into immigration decisions. These were not science-fiction breakdowns. They were policy failures dressed in the language of efficiency.

Private sector misuse has been no better. Amazon famously abandoned an internal AI recruiting tool after it showed bias against women. In the US health insurance sector, companies have faced lawsuits over algorithmic systems allegedly used to deny or limit care decisions at scale. Clearview AI built a business by scraping billions of facial images without consent, turning human faces into a searchable database before society had any meaningful chance to debate the ethics.

The common thread is not that AI became evil. It is that institutions used it in ways that amplified their existing power, opacity and appetite for expedience.

Southeast Asia should pay very close attention

Why should a Singapore-based startup publication care about a frontier AI lab in San Francisco allegedly hacking an AI company in New York? Because Southeast Asia is precisely the kind of region where the consequences of weak AI governance will be imported long before effective protections are built locally.

Many startups here will not train frontier models. They will build on top of them. They will integrate agentic tools into customer service, finance, logistics, healthcare, education, and government services. They will inherit both the capabilities and the failure modes of systems designed elsewhere, often under commercial pressure to ship quickly and ask questions later.

That makes accountability standards non-negotiable. If a model can access the internet, use credentials, discover vulnerabilities and target third-party systems, then every company deploying AI agents needs to treat them less like chatbots and more like junior operators with the potential to create legal, financial and reputational damage at machine speed.

And no, “the model did it” cannot become a valid excuse in boardrooms, procurement meetings or regulatory hearings.

The real divide is not open versus closed

This incident will also inflame the stale open-source versus closed-model argument. But the sharper lesson is not that open models are safer or closed models are safer. It is that concentrated power plus low transparency is a dangerous mix.

When only a handful of companies can inspect the most capable systems, set the test conditions, define the guardrails and narrate the failures, the public is asked to trust institutions that have every incentive to manage perception. That is not a safety regime. That is a branding strategy.

Also Read: Most AI projects don’t fail on technology, they fail on the workflow nobody fixed first

Startups, regulators and enterprise buyers in Southeast Asia should insist on something more boring and far more useful: auditability, liability, independent red-teaming, incident disclosure rules and procurement standards that do not treat frontier model providers as priesthoods.

The OpenAI-Hugging Face incident, if borne out, is not a warning that AI has become too human. It is a warning that the people building it are still too comfortable externalising the risk. That is the scandal. And the longer the industry hides behind the mythology of rogue machines, the more damage it will do before anyone forces it to grow up.

The post “The AI did it” is not a defence; it is a confession appeared first on e27.

For years, cell therapy has carried one of oncology’s most striking contradictions. It has changed the outlook for some blood cancer patients, yet has struggled to make the same impact in solid tumours, which account for the vast majority of cancer cases worldwide.

Singapore-based Tikva Allocell is trying to push through that wall with a different kind of off-the-shelf cell therapy. The biotechnology company has raised US$8 million in Series A financing led by Kantharos Capital, with the proceeds earmarked for studies needed before human testing and a planned Investigational New Drug, or IND, submission by the end of 2026.

Also Read: Singapore’s Biobot Surgical raises US$15.6M to take prostate-care robot global

If regulators clear the application, Tikva plans to begin a Phase 1 clinical trial of its lead candidate, TAVST01, in patients with advanced B7-H3-positive cancers at sites in Singapore and the US.

That dual geography matters. For Singapore, which has spent years building its biomedical research base, the trial would place a homegrown cell therapy company on a path that connects local clinical infrastructure with the world’s largest biotech market. For patients, the more important question is whether Tikva’s approach can solve a problem that has repeatedly defeated the field: how to make donor-derived immune cells survive long enough inside a patient to attack solid tumours.

A different starting point for cell therapy

TAVST01 targets B7-H3, a protein found across several difficult-to-treat solid tumours, including lung, breast, prostate, pancreatic and paediatric cancers. B7-H3 has attracted interest because it is often highly expressed on cancer cells and in the tumour microenvironment, while its presence in normal tissues appears more limited, making it a potential target for cancer therapies.

The company’s approach begins not with a generic donor T cell, but with Epstein-Barr virus-specific T cells. Epstein-Barr virus, or EBV, is extremely common; most adults carry it from a past infection, and the immune system typically keeps a long-lived population of EBV-fighting T cells on patrol. Tikva’s bet is that these cells may offer the durability that conventional donor-derived cell therapies have lacked.

“Cell therapy has transformed the treatment of blood cancers but has repeatedly stalled at the solid-tumour door; the donor cells either fail to persist or are eliminated by the patient’s immune system before they can act,” said Ivan Horak, founder and CEO of Tikva Allocell.

“We started from a different place: a virus-fighting T cell the body naturally sustains, armed to seek out B7-H3 and engineered to withstand the rejection that defeats most donor-derived approaches, with minimal gene editing,” he added.

Also Read: AI is detecting cancer earlier in Southeast Asia but our policies and capital have not caught up

Tikva’s platform, called ALLO SerpinB9 EBVST, is licensed exclusively from Baylor College of Medicine and further enhanced through the company’s own protein-engineering work. The cells are fitted with a B7-H3-targeting receptor and an optimised version of SerpinB9, a naturally occurring inhibitor of granzyme B. Granzyme B is one of the enzymes immune cells use to kill their targets.

In simple terms, a patient’s immune system would normally recognise donor cells as foreign and attack them. Tikva’s SerpinB9 “armour” is designed to help the therapy resist that attack, remain active for longer, and reduce one of the central weaknesses of allogeneic, or donor-derived, cell therapy. The company also says the approach is designed to minimise graft-versus-host disease, a serious complication in which donor immune cells attack the patient’s healthy tissues, while requiring only limited gene editing.

Why solid tumours remain hard

The promise of cell therapy is best known through CAR-T treatments, where a patient’s immune cells are engineered to recognise cancer and then infused back into the body. These therapies have delivered strong results in some blood cancers, but solid tumours are a different battlefield.

Tumour masses are physically harder for immune cells to penetrate. They often create an immunosuppressive microenvironment, a local shield that weakens immune attacks. Antigens, the markers therapies use to identify cancer cells, can vary across tumour cells, allowing some cancer cells to escape. And when cells come from a donor rather than the patient, the recipient’s immune system may quickly eliminate them.

Allogeneic therapies are attractive because they can be manufactured in advance, stored, and potentially given to many patients without waiting weeks for a bespoke treatment. That could make them cheaper, faster and more scalable than patient-specific therapies. But the trade-off has been persistence: if the donor cells disappear too quickly, they may not have time to do meaningful work.

Tikva’s answer is to use a type of immune cell the body is already used to maintaining, then engineer it to both recognise B7-H3 and resist immune rejection. In preclinical work, the company says TAVST01 has shown potential to kill tumour cells directly and to remodel the tumour microenvironment that has held back other solid-tumour cell therapy attempts.

The next step is more demanding. IND-enabling studies will test whether the therapy is safe enough, consistent enough and well-characterised enough for regulators to allow human trials. For a young biotech, this stage is capital-intensive and unforgiving, which makes the Series A round central to Tikva’s timetable.

A Singapore biotech with global ambitions

Tikva’s financing also reflects a broader shift in Southeast Asia’s life sciences ecosystem. The region is better known in tech circles for fintech, e-commerce and logistics startups, but Singapore has long treated biomedical science as a strategic sector, supported by research institutes, hospital networks, manufacturing capacity and regulatory infrastructure.

Still, building a biotech company in Southeast Asia is very different from building a software startup. Timelines are longer, capital requirements are heavier, and the path to revenue usually runs through clinical data, regulatory approval and partnerships with larger pharmaceutical companies. For Singapore-based biotechs, the challenge is not only to do credible science, but to connect early research with global clinical and commercial pathways.

Tikva appears to be structuring itself with that in mind. By planning clinical sites in both Singapore and the US, it can anchor development in its home market while engaging the regulatory and clinical ecosystem that often determines whether biotech assets attract global investors, partners or acquirers.

“Our investment reflects strong conviction in both Tikva’s science and its leadership team,” said Terence Tan, Managing Partner at Kantharos Capital. “Tikva is addressing fundamental challenges that have constrained allogeneic cell therapies, and we believe its ALLO SerpinB9 EBVST platform can extend the reach of cell therapy to solid-tumour patients who today have limited options.”

The competitive field

Tikva is entering a crowded and technically difficult race. Globally, companies such as Fate Therapeutics, Allogene Therapeutics, Caribou Biosciences, and Atara Biotherapeutics have explored allogeneic cell therapies, while larger pharmaceutical and biotech players continue to invest in CAR-T, T-cell receptor therapies and natural killer cell platforms. In solid tumours, B7-H3 is also being pursued through different modalities, including antibody-drug conjugates, bispecific antibodies and cell therapies. That means Tikva will not be judged on novelty alone. It will need to show that its EBV-specific, SerpinB9-armoured cells can persist, avoid serious safety issues, and generate signals of tumour activity in patients who have few remaining options.

Also Read: Danish startup Alcolase is betting its future on Asia’s drinking culture

For now, the company’s story remains preclinical. The US$8 million round does not prove that TAVST01 will work in humans, nor does it remove the biological risks that have humbled many solid-tumour programmes before it. But it does give Tikva enough runway to test a clear hypothesis: that a naturally persistent virus-specific T cell, properly engineered, can become a practical off-the-shelf weapon against solid cancers.

If that hypothesis survives clinical testing, the implications would reach well beyond one Singapore startup. It would strengthen Southeast Asia’s claim to a place in high-end therapeutic innovation, not merely as a trial site or manufacturing base, but as a source of globally relevant biotech platforms.

The post Singapore’s Tikva targets solid cancer barrier with US$8M Series A appeared first on e27.

When OpenAI recently disclosed that Hugging Face had been breached through access to its pre-release models, the instinctive question was technical: how did the system fail? But the more important question was human: who decided to grant that access, under what safeguards, and who owns the consequences?

That question sits at the heart of one of the most corrosive ideas spreading through boardrooms across Asia, that when an AI system causes harm, the humans behind it bear diminished responsibility. They do not.

Every AI deployment is a chain of human decisions: what data to use, what outcomes to optimise for, what risks to accept, and what oversight to build in. When that chain produces harm, it does not matter that a model made the final call. The people who designed, deployed, and profited from that system made it possible.

In Southeast Asia, where AI is being embedded into lending, hiring, healthcare, and content moderation at speed, this accountability gap is not theoretical. It is a liability hiding in plain sight.

Blaming the AI is not a defence. It is a confession.

REGIONAL

Grab backs Vietnam EV charging startup EBOOST: The ride-hailing giant’s strategic investment in EBOOST signals its intent to anchor EV infrastructure in Vietnam as competition among charging networks intensifies across the country.

MAS pushes digital assets beyond speculation as Coinbase expands: Singapore’s MAS is broadening its digital assets framework beyond crypto trading, aligning with Coinbase’s Singapore expansion to position the city-state as a serious institutional digital-asset hub.

Maribank receives approval to launch in the Philippines: Sea Limited’s digital bank Maribank is set to become the Philippines’s seventh digital bank, deepening Sea’s financial services footprint across Southeast Asia beyond its Singapore base.

Omio raises US$10M to expand into Japan and SEA: The European multimodal travel booking platform secured fresh funding to enter Japan and Southeast Asia, targeting fragmented regional rail and ferry markets underserved by existing OTAs.

Vietnam proposes banning under-16s from posting on social media: A draft law targeting minors’ social media activity would prohibit those under 16 from publishing content, adding Vietnam to a growing list of governments tightening youth online protections.

Monks Hill Ventures shuts Indonesia office: The Singapore-based VC’s closure of its Jakarta office reflects a broader pullback in SEA venture activity, raising questions about long-term institutional commitment to Indonesia’s startup market.

Lazada founder raises capital for new AI-first wealth startup: Pierre Poignant, co-founder of Lazada, is building an AI-driven wealth management platform, betting that Southeast Asia’s growing affluent class remains underserved by traditional private banking.

GoRocky acquires Kindred to expand Philippine telehealth: Philippine telehealth startup GoRocky’s acquisition of Kindred consolidates the country’s digital health market as demand for remote medical services continues to outpace supply of licensed practitioners.

Singapore refill startup Ecoworks secures Lam Soon investment: Ecoworks, which makes concentrated refill cleaning products, has drawn strategic backing from FMCG group Lam Soon, signalling growing corporate appetite for sustainable consumer goods in Singapore.

Hong Kong pitches SEA founders on ‘super partner’ positioning: Hong Kong’s bid to reframe itself as a strategic gateway rather than a rival to Singapore reflects intensifying competition for regional startup and capital flows.

Singapore’s startup rise sharpens focus on corporate venturing: A surge in Singapore-based startups is drawing more corporates into early-stage investing, with corporate VC arms competing more directly with traditional financial VCs for deal access.

Malaysia and Hong Kong ink capital markets collaboration deal: The two markets formalised a deal to strengthen cross-border capital market connectivity, a move that could ease dual-listing pathways and cross-border fundraising for SEA founders.

South Korea fines TikTok US$7M for ad data violations: Korea’s personal data protection regulator levied the fine over TikTok’s use of personal data for targeted advertising without adequate user consent, the latest regulatory strike against the platform in Asia.

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INTERVIEWS & FEATURES

“The AI did it” is not a defence; it is a confession: As AI is embedded into consequential decisions, accountability cannot be outsourced to the model; those who deploy it own the outcomes, full stop.

Wiz AI’s Jennifer Zhang exits to build US AI startup: Zhang’s departure from the president role at Wiz AI to launch her own venture underscores the pull of the US AI market even for operators embedded in Southeast Asia’s ecosystem.

B Capital names Andrew Jackson as first Chief AI Officer: The Singapore-rooted VC’s appointment of a dedicated Chief AI Officer and General Partner signals a structural shift in how top-tier funds are integrating AI into investment operations.

Corporate VC vs financial VC: what Applied Ventures offers founders: An inside look at Intel’s venture arm makes the case that strategic value — supply chain access, co-development, and distribution — matters as much as capital for deep tech startups.

An honest look at SEA venture in 2026: A candid investor perspective on where SEA VC stands mid-year, including which sectors still attract conviction capital and where the funding drought shows no sign of easing.

Deeptech and a fracturing world: SEA needs a new playbook: As geopolitical fault lines redraw global supply chains, this feature argues SEA deep tech founders must rethink go-to-market, funding, and manufacturing strategies built for a more integrated world.

Digital nomads find SEA’s welcome mat has fine print: Visa rules, tax obligations, and bureaucratic friction are quietly eroding Southeast Asia’s appeal as a remote work destination despite governments’ public enthusiasm for attracting global talent.

A playbook for entering Indonesia: localise, partner, adapt: Practical market-entry guidance for founders targeting Indonesia stresses that cultural localisation and deep local partnerships matter far more than product quality alone.

Ropedia raises US$22M to build data layer for real-world robots: Singapore-based Ropedia secured Series A funding to develop the data infrastructure that enables robots to interpret and navigate unstructured real-world environments, a foundational bottleneck for commercial robotics.

Singapore’s data analysts trust AI to work, not to think: A survey of Singapore-based data professionals finds practitioners are comfortable delegating execution to AI tools but remain deeply reluctant to cede analytical judgement or strategic interpretation.

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INTERNATIONAL

Travis Kalanick’s robotics firm raises US$1.7B led by a16z: The former Uber CEO’s new venture secured one of the year’s largest robotics rounds, with Andreessen Horowitz leading, a signal that autonomous systems are drawing serious institutional capital again.

SoftBank nears US$40B loan syndication for OpenAI: The Japanese conglomerate is close to finalising a massive credit facility to fund OpenAI’s expansion, underscoring SoftBank’s deepening financial entanglement with the world’s most prominent AI lab.

OpenAI’s AI spending reaches US$750B: OpenAI has disclosed cumulative AI infrastructure expenditure of US$750B, a figure that illustrates the extraordinary capital intensity of frontier model development and the gap widening between top-tier labs and everyone else.

ServiceNow invests US$40M in India’s BusinessNext for APAC banking: The US enterprise software firm’s strategic investment targets autonomous banking deployment across Asia Pacific, with financial institutions in SEA among the primary target markets.

Google justifies AI spending with booming cloud revenue: Alphabet’s latest earnings show Cloud revenue accelerating sharply, giving Google the financial cover to continue heavy AI infrastructure investment, with direct implications for SEA cloud pricing and enterprise adoption.

Tesla’s robotaxi programme stalls amid operational setbacks: Early operational data from Tesla’s robotaxi rollout shows the service underperforming expectations, a cautionary data point for SEA mobility startups tracking autonomous vehicle timelines.

Jack Dorsey launches Buzz to take on Slack with AI agents: Buzz integrates AI agents directly into team communication workflows, positioning it as an agentic-first alternative to Slack as enterprise AI adoption accelerates.

Meta exits major clean energy pact as gas buildout grows: Meta’s withdrawal from a prominent clean energy alliance signals a strategic pivot toward natural gas to power its AI data centres, a tension SEA governments will watch as they negotiate data centre energy commitments.

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CYBERSECURITY

AI phishing is making trust APAC cybersecurity’s weakest link: Hyper-personalised AI-generated phishing attacks are exploiting the social trust norms prevalent in APAC business culture, making the region disproportionately vulnerable compared with Western markets.

AI already inside the enterprise. Has Asia’s security kept up?: An assessment of enterprise AI adoption in Asia finds security infrastructure lagging significantly behind deployment speed, with shadow AI use creating blind spots in corporate risk management.

OpenAI says Hugging Face was breached via pre-release models: OpenAI disclosed that Hugging Face systems were compromised through access to pre-release models, exposing vulnerabilities in how AI model-sharing platforms manage access controls and pre-deployment security.

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SEMICONDUCTOR

AI chip startup Etched hits US$10.3B valuation: Etched, which builds chips purpose-built for transformer models, reached a US$10.3B valuation backed by prominent investors, a major vote of confidence in application-specific AI silicon over general-purpose GPU architectures.

AMD secures Anthropic investment to challenge Nvidia in AI chips: The Anthropic-AMD partnership to develop alternative AI training chips is the clearest signal yet that major AI labs are actively funding supply-chain diversification away from Nvidia dependency.

South Korea’s chipmakers eye opportunities amid US-China tensions: Korean semiconductor firms are repositioning to capture market share as US-China chip restrictions create gaps in the global supply chain, with SEA nations emerging as potential beneficiaries of supply chain rerouting.

Singapore’s Tikva Allocell raises US$8M Series A: Tikva Allocell, a Singapore-based biotech developing cell therapy manufacturing technology, secured Series A funding led by Kantharos Capital to scale its proprietary cell processing platform.

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AI

Anthropic updates Claude voice mode with stronger models: Claude’s upgraded voice mode now runs on more capable underlying models, narrowing the gap with OpenAI’s Advanced Voice Mode and intensifying competition in the real-time conversational AI segment.

Most AI projects fail on workflow, not technology: Organisations rushing to deploy AI without first redesigning underlying processes are setting themselves up for failure — a pattern particularly prevalent among SEA enterprises adopting AI for the first time.

AI-powered automation reshaping SME operations in SEA: Small and medium enterprises across Southeast Asia are deploying AI to automate back-office functions, with early adopters reporting measurable gains in operational efficiency and cost reduction.

How AI is dismantling the risk pool in insurance: AI-driven hyper-personalisation of insurance pricing is eroding the actuarial foundations of traditional risk pooling, with significant implications for insurtech regulation and access to coverage across SEA.

AI empowered teams are shrinking and that’s harder than it sounds: As AI tools enable smaller teams to do more, the organisational and human costs of headcount reduction are proving far more complex than efficiency metrics suggest.

The end of headcount as a success metric: Venture-backed startups and their investors are rethinking how scale is measured as AI enables leaner teams to generate outsized output, challenging a decade of growth-stage hiring orthodoxy.

The real difference between OpenAI and Anthropic: Beyond capability benchmarks, the divergence between OpenAI and Anthropic’s strategies becomes starkest when AI becomes a commodity — one built for scale, the other for trust.

Taiwan’s stablecoin moment: NTD could outshine the dollar: A case for Taiwan dollar-backed stablecoins argues that Taiwan’s trade surplus, currency stability, and semiconductor-driven export economy make it a compelling anchor for a non-USD stablecoin, relevant as SEA explores digital currency infrastructure.

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THOUGHT LEADERSHIP

SEA startups can no longer sit on the fence on tech stack: Geopolitical fragmentation is forcing SEA founders to choose sides on cloud, chip, and software infrastructure, a decision with long-term strategic consequences they can no longer defer.

Southeast Asia as a climate capital proving ground: SEA is emerging as the test bed for climate finance models that blend blended finance, carbon markets, and impact investing, a convergence that could attract a new class of global capital.

Thought leadership as a market entry tool: Founders expanding beyond their first market can use strategic content and public positioning to build trust and brand credibility in new geographies before sales teams arrive.

The most sophisticated AI strategy is a puzzle hunt in Toa Payoh: Singapore’s grassroots AI adoption story, told through a community scavenger hunt, argues that the most durable AI strategies are built bottom-up, not imposed top-down by strategy consultants.

Multi-channel marketing is survival, not strategy, in Asia: Single-channel dependency is a growth ceiling for Asian brands. This piece makes the case that diversified marketing is now a baseline operational requirement, not a competitive differentiator.

Why remote no longer means anywhere: The era of frictionless remote work is ending as employers tighten location requirements and compliance burdens grow, with real consequences for SEA’s talent mobility and hiring models.

Product management as method acting: The best PMs don’t just research users; they internalise them, adopting their mental models and constraints to build products that solve real problems rather than assumed ones.

The post Ecosystem Roundup: When AI acts, humans are still responsible appeared first on e27.

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

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

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

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

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

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

The new face of social engineering

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

What AI changes is the quality and scale of deception.

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

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

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

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

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

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

Training has not caught up

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

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

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

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

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

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

Why APAC firms face a sharper test

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

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

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

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

From blocking threats to building judgement

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

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

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

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

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

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

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

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

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

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

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

From crypto trading to financial plumbing

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

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

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

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

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

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

What BLOOM is trying to solve

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

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

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

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

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

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

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

Coinbase in a crowded regional race

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

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

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

The next test: moving beyond pilots

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

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

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

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

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

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

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

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

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

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

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

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

Asia’s fragmented travel map

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

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

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

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

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

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

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

Why Granite-Integral matters

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

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

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

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

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

Rivals chasing the same traveller

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The post Corporate VC vs financial VC: What Applied Ventures offers founders that cash can’t buy appeared first on e27.

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

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

The point is not to imagine the user better

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

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

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

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

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

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

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

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

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

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

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

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

This approach improves prioritisation because it changes what feels expensive

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

Method acting helps correct that.

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

Product teams should immerse themselves in pairs, not in isolation

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

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

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

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

The risk is romanticising pain instead of removing it

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

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

That is where this whole idea becomes self-indulgent.

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

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

The deepest insight often comes from repeated immersion

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

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

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

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

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The post Product management as method acting: Becoming your user appeared first on e27.