Kaspersky researchers say they have uncovered a long-running cyber-espionage campaign targeting government and diplomatic organisations in Southeast Asia, using a Go-based remote access trojan designed less for smash-and-grab theft than for patient intelligence collection.

The campaign, which Kaspersky’s Global Research and Analysis Team has dubbed GoSerpent, was identified in July 2026. According to the company, the operation uses a customised toolset that includes the GoSerpent backdoor, Stowaway, and TmcLoader, suggesting a campaign built for persistence, stealth, and staged data exfiltration.

Also Read: After cyber attacks, silence can be the biggest brand killer: Penta’s Dan La Russo

The finding lands in a region where state-linked cyber operations have become part of the wider geopolitical weather. Southeast Asia sits between major powers, hosts critical shipping lanes, has a dense web of diplomatic missions, and is rapidly digitising public services. For attackers seeking policy intelligence, trade positions, defence information, or diplomatic cables, ministries and embassies in the region are high-value targets.

A campaign built around patience

Kaspersky described GoSerpent as a sophisticated remote access trojan that has been active since at least 2021, with the latest known variant deployed this year. Written in Go, the malware uses persistence mechanisms and filenames that mimic legitimate system processes, a familiar but effective trick to reduce visibility inside compromised systems.

What makes this campaign notable is not just the tooling but the tempo. Rather than immediately deploying every payload after gaining access, the attackers appear to wait before moving to secondary tools used for exfiltration.

“What stands out about GoSerpent is the deliberate dwell time. Usually, attackers want to move quickly once they get a foothold, but this group drops the initial backdoor and waits,” said Noushin Shabab, Lead Security Researcher in Kaspersky GReAT. “They let the dust settle for weeks before deploying their secondary exfiltration tools like TmcLoader.”

That delay, she added, is designed to outlast standard log retention policies and automated security sweeps, making it harder for defenders to connect the first compromise with the later theft of data.

For under-resourced government agencies, this is a serious problem. Many public-sector systems in the region still run on uneven security budgets, fragmented vendor environments, and legacy infrastructure. Even where agencies have endpoint protection and monitoring in place, long dwell times can expose gaps in logging, incident response, and cross-agency threat sharing.

Why Southeast Asia remains a prime target

Kaspersky said the victims were government and diplomatic entities in Southeast Asia, though it did not name specific countries or agencies. That omission is typical in cyber-espionage reporting, where disclosing victims may trigger diplomatic fallout or reveal ongoing investigations.

The regional context matters. ASEAN has placed cybersecurity on the policy agenda through efforts such as the ASEAN Cybersecurity Cooperation Strategy 2021-2025 and the ASEAN-Singapore Cybersecurity Centre of Excellence. Singapore, Malaysia, Thailand, Indonesia, Vietnam, and the Philippines have all expanded national cyber agencies or regulations in recent years. Yet the region remains uneven: Singapore has mature cyber governance, while several neighbours continue to grapple with shortages of skilled personnel, decentralised IT procurement, and weak incident disclosure cultures.

Also Read: Hackers using AI to mask identity behind cyber attacks, researchers say

That unevenness gives advanced threat actors room to operate. A compromised diplomatic network in one country can become a stepping stone to broader intelligence on ASEAN negotiations, defence partnerships, trade deals, maritime disputes, or foreign investment decisions. In the context of the South China Sea, supply-chain realignments, semiconductor policy, and digital trade rules, diplomatic inboxes and internal files are not mere administrative targets; they are intelligence assets.

For Southeast Asia’s startup ecosystem, the lesson is indirect but important. Govtech vendors, cloud service providers, cybersecurity startups, system integrators, and managed service providers increasingly sit inside public-sector supply chains. Attackers do not always need to breach a ministry head-on if a contractor with weaker controls offers a cleaner route in.

Possible link to TetrisPhantom

Kaspersky said it suspects a connection between GoSerpent and the TetrisPhantom threat actor, citing overlaps in victimology, technical capabilities, and operational methods. The company has not made a definitive attribution and said further investigation is continuing.

TetrisPhantom has previously been associated with cyber-espionage activity against government entities in Asia-Pacific, including campaigns that drew attention because of their focus on highly specific targets and operational discipline. A possible link, if eventually confirmed, would reinforce the view that GoSerpent is not a commodity malware campaign but part of a more targeted intelligence operation.

Attribution in cyber-espionage remains a messy business. Security vendors typically rely on infrastructure overlaps, malware similarities, victim profiles, operational timing, and tradecraft. None of these alone is conclusive. Groups also reuse tools, borrow techniques, and deliberately plant false flags. Kaspersky’s cautious wording is therefore notable: the company is flagging similarities without declaring a firm actor behind the campaign.

Beyond tools: the hard part is visibility

Kaspersky’s advisory urges organisations to watch for GoSerpent indicators of compromise and strengthen detection, response, email security, digital footprint monitoring, compromise assessments, and incident response readiness. Stripped of the product language, the underlying point is straightforward: agencies need better telemetry and longer memory.

A campaign that waits weeks between initial access and exfiltration is betting that defenders will lose the trail. Short log-retention windows, siloed security teams, and over-reliance on automated alerts all work in the attacker’s favour. For diplomatic and government networks, where the data value is high and intrusions may be quiet, security teams need the ability to reconstruct events across endpoints, servers, identity systems, and email environments over extended periods.

Also Read: Are cyber attacks more life-threatening than we think?

The GoSerpent disclosure is not a mass-market ransomware story. There is no public claim site, no splashy ransom note, and no immediate operational shutdown. That makes it less visible but arguably more consequential. In espionage campaigns, success is measured by what remains unknown: how long the attacker stayed, what they read, and which decisions they influenced before anyone noticed.

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Singapore is moving to sharpen the rules around how companies use personal data in generative AI, as the country tries to square two competing realities: businesses want more data to build AI systems, while users are increasingly asked to trust tools they barely understand.

At the Singapore Data Festival on Monday, Minister for Digital Development and Information Josephine Teo announced three new initiatives: advisory guidelines on the use of personal data in generative AI, transparency guidelines for AI chatbots, and a Digital Twin for Enterprises Playbook aimed at helping companies deploy real-time virtual models of physical operations.

Also Read: Beyond the hype: What generative AI is actually changing in startups

The announcements are not a dramatic regulatory crackdown. They are closer to a tightening of expectations. But for startups, banks, telcos, SaaS firms, customer service platforms, and AI application developers in Southeast Asia, they point to where Singapore wants the market to go: more disclosure, clearer consent, and fewer vague privacy notices hiding broad data use.

“Without good data, even the best systems will struggle to produce useful outcomes,” Teo said. “That is why data governance matters more, not less, in the age of AI.”

Consent can no longer hide in boilerplate

The most consequential move is the Personal Data Protection Commission’s Advisory Guidelines on the Use of Personal Data in Generative AI.

Under Singapore’s Personal Data Protection Act, organisations already need consent to collect, use or disclose personal data unless exceptions apply. The new guidelines clarify what that means when companies use personal data to develop, improve or fine-tune generative AI models.

Teo used the example of a customer service team wanting to train a generative AI model on call recordings. Such recordings often contain names, addresses, billing information and other personal details. Under the new guidance, companies should not rely on generic wording buried in privacy policies. They should state clearly that customer data may be used to train or improve AI models.

That matters because the generative AI supply chain is messy. A startup may build an app using a third-party large language model, fine-tune it with customer conversations, host it on cloud infrastructure, and integrate analytics from another vendor. When something goes wrong, such as data leakage, hallucinated advice or inappropriate use of public datasets, accountability can quickly become diluted.

The PDPC guidelines also address roles and responsibilities across the AI value chain, as well as due diligence when organisations rely on publicly available data.

Also Read: Without governance, AI agents risk becoming enterprise chaos engines

For Southeast Asia, this is not an abstract compliance problem. The region’s digital economy is projected to reach around US$1 trillion by 2030, according to regional policy and industry estimates, and much of that growth will depend on cross-border data flows, platform trust and AI-enabled services. Yet data protection rules remain uneven across ASEAN, with Singapore, Malaysia, Thailand, Indonesia, Vietnam and the Philippines at different stages of enforcement and regulatory maturity.

Singapore is effectively trying to set the operating standard before bad practices become entrenched.

Chatbots may soon come with ‘information cards’

IMDA is also launching Generative AI Chatbot Transparency Guidelines, beginning as a voluntary framework.

The key idea is a Chatbot Information Card. Teo compared it to the label on medicinal products: not a full technical manual, but a plain-language summary of what the chatbot is for, what it is not for, how data may be handled, and how users can report issues.

This targets a real gap. Most users do not read terms of service documents. Even if they do, the relevant information is often scattered across privacy notices, AI disclaimers and product documentation. For consumer-facing AI services, the result is a dangerous grey zone: users may disclose sensitive information without understanding how it is stored, reviewed or used to improve systems.

DBS, Google, Meta, OCBC and Singapore Airlines are among the early adopters that will use the guidelines as a reference point. Google is expected to consolidate key information about its Gemini app, while Meta will provide clearer information on how users interact with its AI-powered products.

The competitive implications are broader. Gemini competes directly with OpenAI’s ChatGPT, Anthropic’s Claude and Microsoft Copilot, all of which are fighting for enterprise and consumer adoption in Asia. Meta AI is being pushed through social platforms with massive regional reach, particularly in markets where Facebook, Instagram and WhatsApp remain default digital infrastructure.

For banks such as DBS and OCBC, the pressure is also regional. Rivals including UOB, Maybank, CIMB and Kasikornbank are all experimenting with AI across fraud detection, customer engagement and operations. A chatbot transparency norm in Singapore could quickly become a benchmark for financial institutions operating across ASEAN.

Digital twins move beyond large enterprises

The third initiative, IMDA’s Digital Twin for Enterprises Playbook, is aimed at a different but related problem: helping companies turn operational data into useful AI systems.

Also Read: Why emerging markets need AI governance infrastructure before AI scale

A digital twin is a real-time virtual representation of physical assets, systems or processes. Large industrial companies, logistics operators, airlines and Formula One teams have used such systems for years. The government now wants smaller enterprises to see them as practical tools rather than futuristic toys.

Teo cited Exceltec, a Singapore facilities management company that built a digital twin drawing on sensor data from more than 70 customer sites. The system monitors issues such as air-conditioning faults or unusual water usage that may indicate leaks. According to Teo, the system saves each team about 45 minutes a day on each inspection.

Exceltec operates in a crowded facilities management and building services market that includes players such as CBM, C&W Services, ENGIE Services, Sodexo and Surbana Jurong-linked service providers. For smaller operators, digital twins could become a way to compete on predictive maintenance rather than manpower-heavy inspection routines.

Across Southeast Asia, the timing is relevant. Cities are adding sensors to buildings, utilities and transport networks, while property owners face rising energy costs and pressure to improve sustainability reporting. In markets such as Singapore, Malaysia, Thailand and Vietnam, digital twins are likely to be pulled into smart building, manufacturing and logistics use cases.

But the playbook’s “legal guide” framing is telling. The government is not just pushing adoption; it is warning companies that data architecture, consent, security and accountability cannot be bolted on later.

ASEAN context: AI safety is local, not universal

Teo also pointed to January’s AI Safety Red Teaming Challenge, where more than 80 experts from all ASEAN countries, as well as China, India, Japan and Korea, tested whether generative AI applications could leak protected data.

The findings underline a problem global AI companies often underplay: model safety does not travel neatly across languages and cultures. Some harmful requests refused in English were answered in Khmer. Casual local phrasing could bypass safeguards that worked against formal prompts.

Also Read: Safeguarding your organisation in the age of increasing AI

That is a serious issue for Southeast Asia, where hundreds of languages and dialects sit alongside uneven digital literacy and fast AI adoption. Guardrails built primarily for English-speaking users may fail in local contexts.

“None of us can build a trusted data ecosystem by looking only within our own borders,” Teo said.

Singapore will assume the ASEAN Chairmanship next year and has signalled that trusted data use will be part of its regional digital agenda. The challenge will be moving beyond voluntary frameworks and high-level alignment. For startups and enterprises, the direction is already clear: if AI is trained on user data, users need to be told plainly; if chatbots interact with the public, their limits must be visible; and if companies want to extract value from operational data, governance has to start before deployment, not after the first breach.

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Anthropic may look stronger than OpenAI on the usual pre-IPO scoreboard.

It has reported stronger private-market momentum. It appears closer to near-term operating profit. Its gross margin is reported above OpenAI’s. It has deep enterprise relationships, a strong reputation with developers, and Claude Code has become one of the clearest examples of an AI product that customers already pay for at scale.

On the surface, that looks like the cleaner business.

But the more useful question is not which company looks better today. It is which company gets stronger as AI does what everyone expects it to do: improve and get cheaper.

On that question, OpenAI and Anthropic are not the same kind of company.

Anthropic mainly sells access to frontier intelligence. OpenAI sells that too, but it also controls a mass consumer interface used by hundreds of millions of people. That difference matters because falling AI costs do not affect both businesses in the same way. For a frontier model seller, cheaper AI erodes the price of the thing being sold. For a consumer platform, cheaper AI lowers the cost of serving users whose attention can be monetised through advertising, commerce, subscriptions, and referrals.

The same cost curve can damage one business model and strengthen another.

That is the central divergence.

The price of AI work is falling extremely fast. Depending on the benchmark and the task, equivalent-quality AI has been getting cheaper by orders of magnitude. One widely cited estimate puts constant-capability inference cost decline at roughly tenfold per year. Other measurements show even sharper falls for some tasks. Work that once cost tens of dollars per million tokens has moved toward cents.

This is not a normal software pricing cycle. It is the economics of a manufactured input. As models improve, hardware scales, inference systems are optimised, and competition intensifies, the unit cost of producing cognitive work falls. The unusual part is that the manufactured product is not a phone, chip, battery, or solar panel. It is intelligence delivered through computation.

That distinction matters for valuation.

If a company sells a scarce software product with durable pricing power, investors can imagine software margins. But if a company sells a manufactured input on a steep cost curve, the better analogy is not classic enterprise software. It is a commodity producer with a premium tier on top. The premium may be valuable, but it is constantly under attack from the next cheaper substitute.

This is the problem Anthropic has to solve.

Also Read: Your offshore vendor’s AI is running on your code: Do you know which one?

At the moment, frontier models are not good enough for many complete workflows. They still make errors. They need supervision. They lose context. They often complete pieces of work rather than whole jobs. Because of that, each capability improvement increases demand for the newest model. Buyers want the best system because the current one is still not quite enough.

That creates a temporary premium market.

But the demand for capability is bounded by the task. Once a model can perform a defined job to a competent standard, a better model adds less value. If an AI system can take a set of accounting records, identify what matters, apply the rules, produce the filing, flag the judgment calls, and explain the output, then the buyer no longer needs the newest frontier model for that task. The job is done.

At that point, the buyer has a different question: what is the cheapest model that clears the bar?

That is the good-enough threshold. Once a task crosses it, the task leaves the premium market. It falls into the commodity market, where open-weight models, older frontier models, and cheaper specialist systems compete for the work. The frontier model may still be better in a general sense, but better no longer matters enough to command a large price premium for that specific job.

This is the structural trap for a business built around selling frontier access.

Improving the model conquers more tasks. But conquering a task means that task eventually stops needing the frontier. Over time, the frontier-only market does not automatically expand. It may narrow unless new categories of work open faster than old ones commoditise.

Anthropic is not blind to this. Claude Code is important because it moves the company higher up the stack. It is not merely selling tokens. It is selling a specific outcome inside a valuable workflow. That is the right direction. Application-layer products are less exposed than raw model access because customers are paying for the completed job, integration, and workflow value rather than just the intelligence underneath.

But the valuation question remains. How much of Anthropic’s future value comes from durable application products, and how much still depends on frontier access retaining premium pricing?

OpenAI has a different problem and a different opportunity.

It also sells model access. It also faces inference costs. It also competes in the frontier race. But it owns something Anthropic does not: a consumer destination at enormous scale.

That changes the effect of falling AI costs.

Also Read: AI is answering your customers before they ever click, and it may never mention you

For OpenAI, cheaper inference reduces the cost of serving free and low-paying users. If those users can be monetised through advertising, commerce, referrals, subscriptions, enterprise conversion, or in-chat purchasing, then falling AI costs widen the gap between the cost of serving attention and the value of monetising it.

That is not the economics of a pure model vendor. It is the economics of a platform.

This is why OpenAI’s experiments with advertising and in-chat commerce matter. They are not just incremental monetisation ideas. They are attempts to shift the company away from selling tokens and toward monetising the interface where users already spend time, search for answers, compare products, and make decisions.

If ChatGPT becomes a meaningful consumer gateway, then cheaper AI helps OpenAI twice. It lowers the cost of each interaction, and it increases the number of interactions that can be economically served. More usage is no longer only a cost burden. It becomes monetisable surface area.

That is the flywheel OpenAI is trying to build.

The contrast is simple. A tenfold annual fall in inference cost lowers the price of the thing Anthropic mainly sells. The same tenfold fall lowers the cost of the thing OpenAI can give away to attract and monetise users.

One business sells the deflating input. The other may use the deflating input to build a larger platform.

That does not make OpenAI’s outcome guaranteed. Advertising inside an AI assistant could damage user trust. Commerce may not convert at scale. Referral economics may be weaker than expected. Regulators may limit parts of the model. Users may resist a shift from neutral assistant to monetised shopping interface. The consumer flywheel is still a thesis, not a proven revenue engine.

OpenAI also carries its own financial pressure. Serving hundreds of millions of users is expensive, even when unit costs are falling. Infrastructure commitments are large. The company still has to prove that mass usage converts into durable economics rather than just enormous demand for subsidised computation.

But structurally, OpenAI has more ways to benefit from AI deflation.

Also Read: Beyond the chatbot: How Gen Z pioneers are leading ASEAN’s new AI revolution

Anthropic’s strongest counterargument is that frontier capability may stay scarce. If the best models remain meaningfully better, if enterprise customers are deeply locked into workflows, if safety, reliability, compliance, and data integration matter more than raw token price, then premium pricing can survive longer than the simple commodity story suggests. In that version of the market, Anthropic’s enterprise concentration is not a weakness. It is evidence of pricing power.

The second counterargument is that new frontier-only work may open faster than existing work commoditises. If each generation of models enables qualitatively new tasks — autonomous agents, longer-horizon reasoning, richer multimodal work, or entirely new software workflows — then demand for the newest model may keep expanding.

That is the key uncertainty.

The question is whether frontier intelligence remains a scarce product, or whether it becomes a rapidly cheapening input.

If it remains scarce, Anthropic’s model is stronger than the deflation argument implies. If it becomes a cheap input, value moves elsewhere: to compute and energy capacity, to distribution, to consumer interfaces, and to applications that convert cheap intelligence into specific outcomes.

That is why the IPO filings matter. The headline valuation will get the attention. The more important signals will be beneath it.

The first signal is revenue quality. Is reported run-rate revenue gross or net of reseller and partner pass-through? A large gap between gross and net would make the top line look stronger than the underlying economics.

The second signal is gross margin. If margins rise sharply while inference prices keep falling, that supports the view that frontier labs can retain pricing power. If margins remain compressed, the commodity interpretation gains strength.

The third signal is revenue mix. How much comes from raw model access, and how much comes from higher-stack products? For Anthropic, Claude Code and similar workflow products matter because they reduce dependence on frontier access alone. For OpenAI, advertising, commerce, subscriptions, and platform monetisation matter because they show whether consumer distribution can become a real economic engine.

The fourth signal is customer behaviour. If enterprises keep paying for the newest model even after cheaper alternatives become good enough for many tasks, lock-in is stronger than expected. If customers shift workloads aggressively to lower-cost models once capability thresholds are crossed, the frontier premium decays.

The useful conclusion is not that OpenAI is certain to beat Anthropic.

It is that the normal scoreboard may be measuring the wrong thing. Revenue growth, private valuation, filing sequence, and near-term profitability describe the present. They do not answer the more important question: what happens as the product gets cheaper?

Anthropic may be ahead on today’s visible metrics. But if most of its value remains tied to selling frontier access, then it is exposed to the falling price of its own output. OpenAI may look less clean financially today, but if it turns cheaper AI into cheaper user acquisition, cheaper user service, and more monetisable attention, then the same deflation becomes an advantage.

The AI market is usually described as a race to build the best model.

That may be the wrong race to watch.

The durable value may not sit with whoever produces the frontier model at any given moment. It may sit with whoever owns the interface, the workflow, the distribution, and the customer relationship once intelligence itself becomes cheap.

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Southeast Asia‘s AI moment is arriving, but not in English. Across Vietnam, Thailand, and Indonesia, a quiet but consequential shift is under way: researchers, startups, and governments are building large language models trained on local languages, dialects, and cultural contexts, rather than waiting for Silicon Valley to localise its tools.

The stakes are significant. English-centric models systematically underperform in Bahasa Indonesia, Thai, and Vietnamese — languages spoken by more than 400 million people. That gap is now attracting serious capital and policy attention. In Vietnam, homegrown LLM efforts are being backed by state-linked entities and private labs alike. In Indonesia, local language AI has become a national priority tied directly to digital sovereignty. Thailand is investing in Thai-language models to serve its healthcare, legal, and public services sectors.

What’s emerging is not a pale imitation of Western AI stacks, but a distinct regional layer, one that reflects local grammar structures, cultural nuance, and governance priorities. For Southeast Asia’s founders and investors, this is both an infrastructure play and a market-creation opportunity. The developers who own the language layer may ultimately own the region’s AI future.

REGIONAL

SEA’s AI future is being built in local languages: Researchers and startups across Vietnam, Thailand, and Indonesia are developing local-language LLMs, challenging English-centric models that underperform across SEA’s 400 million-plus non-English speakers.

SBI buys majority stake in Coinhako for Singapore digital assets push: Japanese financial giant SBI Group has acquired a majority stake in Singapore-based crypto exchange Coinhako, deepening its foothold in the city-state’s regulated digital asset market.

KWAP moves to contain damage after eFishery fraud shock: Malaysian pension fund KWAP is managing exposure from the eFishery accounting fraud, as the fallout from one of SEA’s most high-profile startup collapses continues to ripple through institutional investors.

eFishery ex-CEOs get sentences cut on appeal: A Jakarta court reduced prison terms for eFishery’s former co-founders, following their conviction over the US$600M fraud that shook Southeast Asia’s agritech sector.

Ant International closes US$1.2B series A with Ant Group and Alibaba: Singapore-based Ant International raised US$1.2B in a series A led by its parent entities, signalling continued commitment to cross-border payments and financial services expansion across SEA.

CMBI and SMBC back Whale’s US$40M series C extension: The enterprise AI firm secured a US$40M series C extension co-led by CMBI and SMBC, funding its push to scale AI-driven solutions across Asian enterprises.

SEA IPO fundraising surges 85% in H1 2026: Capital raised through Southeast Asian IPOs jumped 85% year-on-year in the first half of 2026, according to EY data, pointing to renewed investor confidence in public market listings across the region.

VinFast opens 20 Indonesia e-motorcycle dealerships: Vietnamese EV maker VinFast has launched 20 electric motorcycle dealerships across Indonesia, accelerating its regional two-wheeler rollout in SEA’s largest automotive market.

Vietnam emerges as SEA’s next data centre contender: Vietnam is positioning itself as a major data centre destination as regional capacity shifts and hyperscaler demand grows, according to a BMI analysis.

Malaysia poised to become regional data centre hub: A new report identifies Malaysia as a leading candidate for Southeast Asia’s data centre expansion, driven by land availability, energy policy, and growing hyperscaler interest.

WBBA convenes APAC’s first broadband summit in Bangkok: The World Broadband Association held Asia-Pacific’s inaugural broadband development summit in Bangkok and launched a new AI-Net certification standard for regional connectivity infrastructure.

Maybank foresees visible growth ahead for JustCo: Malaysia’s largest bank says Singapore co-working operator JustCo is on track for meaningful expansion, backed by recovering office demand and new markets.

OpenAI capacity strained by surging Asian startup demand: Asian startups are pushing OpenAI’s infrastructure to its limits, with surging API demand from the region testing the company’s ability to scale supply to fast-growing markets.

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INTERVIEWS & FEATURES

Inside SEA’s AI gold rush: the 20 biggest cheque writers: A deep-dive into the 20 investors deploying the most capital into SEA’s AI sector, mapping who is leading deals, at what stages, and in which verticals.

How Vietnam evolved from execution hub to product powerhouse: A feature tracing Vietnam’s technology industry’s shift from outsourcing centre to homegrown product builder, driven by a new generation of founders with global ambitions.

How Gen Z pioneers are leading ASEAN’s new AI revolution: A profile of young founders across ASEAN who are building AI-native companies that go well beyond chatbot applications, reshaping sectors from logistics to healthcare.

What SEA’s digital economy needs beyond growth: An examination of the structural gaps — governance, trust, and inclusion — holding back Southeast Asia’s digital economy despite strong headline expansion numbers.

GenAI tops SEA finance professionals’ skills wishlist: A new ACCA survey finds generative AI is the top skill Southeast Asian finance professionals want to acquire, though access to quality training remains a persistent gap.

Skills built faster than workplaces redesigned to use them: A Singapore Polytechnic analysis finds that workers are upskilling in AI faster than organisations are restructuring roles and workflows to deploy those skills effectively.

Singapore is not a small market, it is a compressed one: A reframing of Singapore’s market dynamics, arguing that density, purchasing power, and institutional access make it uniquely valuable for startups testing high-value propositions.

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INTERNATIONAL

Anthropic’s US$1.5B copyright settlement approved: A US court approved Anthropic’s landmark US$1.5B settlement with publishers over training data use, setting a significant precedent for how AI firms compensate content creators globally.

Netflix paid US$587M for Ben Affleck’s AI filmmaking startup: The streaming giant acquired Make It, Affleck’s AI-assisted film production company, in one of the largest deals yet at the intersection of Hollywood and generative AI.

CuspAI raises US$450M, hits US$2.6B valuation: Temasek-backed CuspAI closed a US$450M round and launched an AI-powered materials discovery network, positioning itself at the frontier of AI-driven scientific research.

OpenAI fears open-weight models, and it should: An analysis of why OpenAI’s internal anxiety over open-weight competitors reflects a broader strategic vulnerability for US AI dominance as capable open models proliferate globally.

Bitcoin reclaims key technical levels as Ethereum leads gains: Bitcoin recovered critical support levels while Ethereum outperformed the broader crypto market, driven by renewed institutional appetite and improving macro sentiment.

AI’s most important protocol gets easier to use: The Model Context Protocol (MCP), the standard enabling AI agents to interact with external tools, has been updated to reduce integration friction for developers building agentic applications.

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CYBERSECURITY

GoSerpent exposes the quiet cyber war on SEA governments: A newly identified threat actor, GoSerpent, has been conducting sustained cyberespionage campaigns against Southeast Asian government agencies, using stealthy malware strains to exfiltrate sensitive data.

Your offshore vendor’s AI is running on your code: Enterprises using offshore development partners face growing IP and security risks as vendors adopt AI coding tools trained on proprietary client codebases without disclosure.

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SEMICONDUCTOR

Powertech and Broadcom to build US$400M Singapore chip JV: Taiwan’s Powertech Technology will invest US$400M in a Singapore-based advanced chip packaging joint venture with Broadcom, reinforcing Singapore’s role in the global semiconductor supply chain.

Google develops new AI chip to boost Gemini efficiency: Google is building a dedicated chip designed to lower inference costs for its Gemini model family, intensifying the in-house silicon race among US AI hyperscalers.

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AI

Singapore turns AI scrutiny to chatbots, personal data, and digital twins: Singapore’s Personal Data Protection Commission is intensifying oversight of AI deployments involving chatbots and digital twins, signalling stricter data governance expectations for AI developers.

Singapore strengthens privacy tools for AI adoption: The government has released updated privacy-enhancing technology guidelines to help enterprises adopt AI responsibly while complying with personal data protection obligations.

AI is about to blow a hole in ASEAN’s climate targets: Soaring AI energy consumption threatens to derail ASEAN’s net-zero commitments unless the region accelerates the transition to clean baseload power for data centres.

AI is answering customers before they click and may never mention you: Businesses risk being bypassed entirely as AI-powered search and answer engines resolve customer queries without surfacing brand websites or product pages.

Should AI investors diversify into Chinese tech stocks?: An analysis of whether allocating to Chinese AI firms offers a meaningful hedge against rising competition from Chinese models challenging US incumbents.

AI will not fix hiring if it only works for employers: A critique arguing that AI-driven recruitment tools entrench existing power imbalances by optimising for employer efficiency while stripping candidates of transparency and recourse.

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THOUGHT LEADERSHIP

Why boards are getting CEO succession wrong: Poor succession planning is costing companies far more than most boards acknowledge, with gaps in structured process and internal talent development creating avoidable leadership crises.

Global expansion is now about reducing trust costs, not information costs: The real barrier to cross-border growth for startups is no longer market data access but building credibility and institutional trust in unfamiliar ecosystems.

It’s time to rebuild the enterprise house with AI: Enterprises must rearchitect their core operations around AI rather than layering tools onto legacy structures, argues this analysis of AI-driven business transformation.

Finance doesn’t have a math problem, it has an ego problem: The financial services industry’s resistance to AI adoption stems less from technical barriers than from cultural rigidity and leadership identity tied to conventional expertise.

Why DNA is becoming the next platform layer in digital health: Genomic data is emerging as foundational infrastructure for personalised healthcare, enabling AI-driven diagnostics and treatment models that move beyond population-level averages.

SEA’s sustainability opportunity is infrastructure: The path to sustainable growth in Southeast Asia runs through physical and digital infrastructure, not just policy commitments, requiring coordinated capital deployment at scale.

Two years to agentic: comply or grow: Organisations have a two-year window to prepare for agentic AI deployment before it reshapes competitive dynamics and those that only focus on compliance will be outpaced by those that build for growth.

The extinction events in product evolution: A framework examining which product categories face existential pressure from AI-native competitors and what survival looks like for incumbents unwilling to cannibalise themselves.

From tools to partners: the Socratic dream of two butterflies: A philosophical exploration of AI as a thinking partner rather than a productivity instrument, drawing on classical dialogue and Eastern metaphor to reframe human-AI collaboration.

The job you’re studying for might not exist: An urgent challenge to education systems and students to rethink career preparation in an era when AI is eliminating roles faster than institutions can update curricula.

The psychology of believing AI: certainty vs truth: Explores how humans are cognitively primed to over-trust confident AI outputs, and why distinguishing between an AI’s expressed certainty and actual accuracy is a critical skill for practitioners.

The post Ecosystem Roundup: SEA’s AI future is being coded in its own languages appeared first on e27.

For years, much of Southeast Asia’s digital economy has been built around a quiet compromise: if users wanted access to the best technology, they often had to meet it in English. That bargain is beginning to shift.

New usage data around Google’s Gemini suggests that generative AI in the region is increasingly being used in local languages, not just by urban professionals writing emails or developers debugging code, but by farmers, older users, students, creators and small business owners who are more comfortable thinking, speaking and selling in their mother tongue.

Also Read: Beyond the chatbot: How Gen Z pioneers are leading ASEAN’s new AI revolution

The clearest signal comes from Vietnam, where 89 per cent of Gemini prompts are now written in Vietnamese. Thailand is close behind, with 87 per cent of prompts in Thai, while Indonesia records 84 per cent in Indonesian.

The figures point to a deeper change in how AI is being adopted across Southeast Asia: usefulness is no longer simply about model size or speed, but about whether the system understands the language, idioms and cultural context of the person using it.

That matters in a region where English has long been treated as the default language of technology, even though it is not the default language of daily life for hundreds of millions of people.

The rise of native-language AI

The shift is not just anecdotal. The report cites the Southeast Asia Holistic Evaluation of Language Models, or SEA-HELM, a benchmark that assesses how large language models perform across regional languages. Gemini is ranked as the best-performing large language model overall for Southeast Asian languages in the evaluation, which covers Burmese, Filipino, Indonesian, Malay, Tamil, Thai, and Vietnamese.

For founders and developers, the implications are practical. A chatbot that works well in English may serve a bank’s urban customers, but it will not necessarily help a farmer in northern Vietnam, a shopkeeper in rural Thailand or a student in an Indonesian public school. To reach those users, AI products need to understand not only grammar and vocabulary, but also local phrasing, intent and cultural references.

This is where native-language performance becomes more than a technical milestone. It expands the addressable market for startups building education tools, financial services, health access platforms, customer support agents, creator tools and productivity apps. In a region as fragmented as Southeast Asia, language has often been a barrier to scale. Better multilingual AI could turn it into a distribution advantage.

Malaysia shows a slightly different pattern. English remains dominant for professional and coding-related tasks, reflecting the country’s multilingual workforce and its long-standing role as a regional services hub. But prompts in Malay have doubled in early 2026, according to the report. That suggests users are not abandoning English so much as switching languages depending on the task: English for work, Malay for learning, creativity or cultural expression.

A tea farmer and the economics of translation

The most compelling example in the source material comes from Lao Cai, a mountainous province in northern Vietnam known for its highland communities and ancient Shan Tuyet tea trees.

A small-scale tea farmer there once depended on middlemen to reach foreign buyers. The problem was not merely logistics; it was language. Selling premium tea to customers in Europe or the US requires more than listing weight and price. It involves storytelling, product descriptions, tasting notes, invoices and trust-building communication. Without fluent English, the farmer was stuck at the edge of the value chain.

Using Gemini, he can now describe his thoughts in Vietnamese and ask the AI to rewrite them in English “like an expert tasting a fine wine”. The output gives him polished descriptions that help position his tea for international buyers while preserving the authenticity of his own story.

This is a small example, but it captures why language-capable AI could matter for Southeast Asia’s small businesses. Translation has traditionally been treated as a support function. In practice, it can determine who captures value. If a farmer, craft producer, homestay owner or independent creator can communicate directly with global customers, they may keep more of the margin that previously went to intermediaries.

Also Read: Indonesia’s AI momentum: Big investments, bigger questions

For startups, this opens space for tools that combine AI translation with payments, logistics, compliance, product photography, storefront creation and customer relationship management. The opportunity is not simply to build another chatbot, but to help local businesses cross borders without losing their voice.

Culture is harder than vocabulary

Localisation is often described as a language problem. In Southeast Asia, it is also a cultural one.

In Malaysia, the report highlights marketing coordinators using Gemini in Malay to brainstorm visual concepts that capture the jiwa, or soul, of local culture. Terms such as lepak, referring loosely to the relaxed act of hanging out, or the familiar glow of kopitiam lighting, carry emotional weight that does not survive cleanly in literal English translation.

This distinction matters for the region’s creative economy. Southeast Asian brands increasingly want to participate in global digital culture without flattening their identity into generic international English. AI tools that understand local nuance could help agencies, content creators and small brands produce work that feels specific rather than templated.

It also matters for inclusion. In Thailand, the report notes that users over 54 are the most multimodal age group, using voice and image prompts in Thai to navigate daily tasks. That hints at another frontier for AI adoption: people who may not type comfortably, may not speak English, or may prefer to show the AI something rather than describe it.

In markets where ageing populations, rural connectivity gaps and uneven digital literacy remain real constraints, voice and image-based AI in local languages could be more transformative than text-only productivity tools aimed at office workers.

Rivals are racing for the same multilingual future

Gemini’s regional language performance puts Google in a strong position, but it is far from alone. OpenAI’s ChatGPT remains widely used across Southeast Asia, especially among English-speaking professionals, students and developers. Anthropic’s Claude has gained traction for writing and analysis-heavy workflows, while Meta’s open-source Llama models are attractive to developers and enterprises that want more control over deployment. Singapore’s AI Singapore has also developed SEA-LION, a family of language models focused on Southeast Asian contexts.

The contest will not be won only by benchmark scores. Distribution, pricing, developer tools, enterprise trust, government relationships and data governance will all matter. In Southeast Asia, one additional factor may prove decisive: whether the model can handle the region’s messy linguistic reality, where users mix English, local languages, dialects, slang and visual cues in the same conversation.

Why this matters for Southeast Asian startups

The Philippines remains an outlier in the Gemini report, with 90 per cent of prompts currently in English, the highest share in the region. That reflects the country’s strong English-language education base and its role in outsourcing, customer support and global services. But the broader direction is clear: the regional internet is becoming more multilingual, not less.

For startups, this changes product assumptions. Interfaces built only for English-speaking urban users will miss large segments of the market. Customer support bots will need to handle code-switching. Education apps will need to explain concepts in the language students use at home. Commerce platforms will need product descriptions that work across borders but begin in local speech.

Also Read: How Vietnam is emerging as a leading AI builder ecosystem in Southeast Asia

The bigger point is that AI adoption in Southeast Asia may not follow the same path as in the US or Europe. Here, the breakthrough use case may be less about replacing white-collar workflows and more about removing the language barriers that have kept millions of people from fully participating in the digital economy.

If generative AI can speak the language of the user’s home, it may become not just a productivity tool, but infrastructure for a more inclusive regional internet.

The post Southeast Asia’s AI future is being written in Vietnamese, Thai, Indonesian appeared first on e27.

Artificial intelligence is no longer a distant promise for Southeast Asia; it is the single most consequential bet being made across the region’s venture capital landscape. In 2025, AI startup investment in Vietnam alone surged 13-fold to US$130 million, while Singapore continued to serve as the region’s capital allocator, with Temasek deploying US$34 billion globally and AI-native software emerging as the dominant investment thesis from seed to growth stage.

Across the six core markets of Singapore, Malaysia, Indonesia, the Philippines, Thailand, and Vietnam, a new generation of VC firms is reshaping how capital flows to founders building with and for artificial intelligence.

This listicle profiles 20 VC firms operating in Southeast Asia that have demonstrated a clear and documented commitment to investing in AI companies, whether through dedicated AI funds, AI-focused cohorts, or consistent backing of AI-native startups across their portfolios.

Each firm has been featured in e27 coverage and verified to be actively deploying capital in the region.

1. Vertex Ventures Southeast Asia & India

Vertex Ventures Southeast Asia & India is one of the region’s most established multi-stage VC firms, backed by Temasek Holdings. Known for its early bet on Grab, Vertex has built a pan-regional portfolio spanning Singapore, Malaysia, Indonesia, Vietnam, the Philippines, and Thailand.

Also Read: In Vietnam, the challenge isn’t talent but mindset, says Vertex’s Genping Liu

The firm operates with a founder-first philosophy, providing hands-on operational support from seed through to growth stage. In recent years, Vertex has deepened its focus on AI-native startups, enterprise software, and deep tech, reflecting the broader shift in SEA’s investment landscape toward technology companies with durable competitive moats.

Notable AI investments & activity: Portfolio includes Grab (AI-powered super-app), multiple enterprise AI and deep tech companies across SEA. Vertex Japan launched with a US$67M fund in March 2025 to connect Japanese AI startups with SEA markets.

2. Wavemaker Partners

Wavemaker Partners is a leading Singapore-based VC firm with dual presence in Southeast Asia and Los Angeles. Since 2012, the firm has backed over 200 companies and manages more than US$600 million across three fund families: Wavemaker Ventures (early-stage B2B tech), Wavemaker Impact (climate-tech), and Wavemaker Growth (bridging Series B gaps).

Wavemaker is particularly known for its thesis-driven approach to enterprise AI, deeptech, and sustainability, and has been one of the most active writers of early-stage checks in the region.

In November 2024, the firm launched a US$60 million growth fund specifically to support SEA’s most promising Series B-stage companies.

Notable AI investments & activity: Backed Carousell, Funding Societies, and multiple AI-native enterprise startups. Wavemaker Ventures led a US$4M round in DataMasque (data privacy AI) in June 2026. Wavemaker Growth Opportunities Fund targets AI-driven B2B companies at Series B.

3. Monk’s Hill Ventures

Monk’s Hill Ventures is a Singapore-based pan-Southeast Asia VC firm that focuses on early-stage technology companies. With 77 investments to its name, the firm is known for leading Series A rounds in the US$2M-US$10M range and providing deep operational support to its portfolio companies.

Monk’s Hill has consistently backed companies at the intersection of AI, deeptech, and enterprise software, and has been a co-investor alongside global tier-one funds including Sequoia and Lightspeed. The firm’s portfolio includes Glints, Lendingkart, and a growing cohort of AI-native B2B startups across the region.

Notable AI Investments & Activity: Led a US$28.8M round in Cinch (device-as-a-service, AI-powered) in April 2025. Co-invested with Iterative in Vietnamese AI wealth management startup 1Long (2024). Active in AI & deep tech, advertising & marketing, and enterprise software.

4. Golden Gate Ventures

Golden Gate Ventures is one of the region’s most prolific early-stage venture capital firms, with 128 investments across the region. Founded in Singapore, the firm has built a reputation for backing category-defining companies at the earliest stages. Golden Gate invests across a broad range of sectors with a strong emphasis on AI and deep tech, and has been an active participant in the region’s AI investment wave. The firm’s extensive network and deep market knowledge make it a preferred first institutional check for founders building AI-native companies in SEA.

Notable AI investments & activity: Portfolio includes Carousell (AI-powered marketplace), Carro (AI-driven auto platform), and multiple AI-native startups. Active investor in AI & deep tech across pre-seed, seed, and Series A stages in SEA.

5. Jungle Ventures

Jungle Ventures is a Singapore-headquartered investor with 151 investments across India and Southeast Asia. The firm has carved out a strong position as a lead investor in AI-native software companies, enterprise SaaS, and consumer technology. Jungle’s investment thesis has increasingly centred on companies that use AI to create defensible, scalable businesses in SEA’s diverse markets. In November 2024, Jungle published a widely cited report on seed investment trends in Asia, noting that median deal sizes are rising even as deal counts stabilise, a signal of increasing conviction in AI-first founders.

Also Read: Median rises, deals dip: Jungle Ventures unpacks seed investment trends in Asia

Notable AI investments & activity: Active in AI-native software, enterprise SaaS, and consumer AI. Published landmark report on seed investment trends in Asia (2024), highlighting AI as the dominant investment theme. Portfolio spans India and SEA with AI focus.

6. Quest Ventures

Quest Ventures is a Singapore-based pan-Asia VC firm with 119 investments across multiple verticals. The firm is known for its multi-sector approach and active presence in markets ranging from Singapore and Southeast Asia to Kazakhstan and the broader Asia-Pacific region. Quest has backed AI-powered startups across healthcare, robotics, IoT, and enterprise software, and has co-invested with global funds to support founders building with AI. The firm also played a key role in establishing a startup and innovation ecosystem partnership with the National Development Commission of the Philippines in 2023.

Notable AI investments & activity: Backed Vulcan Augmetics (AI-powered robotic prosthetics, 2023), Dolbomdream (AI-integrated IoT hugging vest, 2024), and multiple AI-native startups. Active in AI & deep tech, advertising & marketing, and enterprise software.

7. Antler

Antler is a global early-stage VC firm headquartered in Singapore with one of the most active AI investment programmes in Southeast Asia. Through its AI Disrupt programme, launched in March 2025, Antler specifically targets founders building AI-native companies with early commercial traction.

In the second half of 2025, Antler invested US$7.4 million into SEA startups, with US$2.8 million earmarked specifically for AI ventures. In December 2025, the firm deployed US$5.6 million across 14 AI startups in a single cohort. Antler’s portfolio spans over 1,000 investments across six continents, with a particularly strong pipeline in Malaysia, Singapore, Indonesia, and Vietnam.

Notable AI investments & activity: US$5.6M invested in 14 AI startups in one cohort (Dec 2025). US$2.8M for AI ventures in H1 2025. Backed Zeya Health (AI healthcare admin, Singapore), M3TRIQ (AI biotech, Malaysia), NCSpeech (AI fintech, Malaysia), Obiguard (AI governance, Malaysia), Otonoco AI (GenAI compliance, Malaysia).

8. Iterative

Iterative is a Singapore-based seed-stage VC firm that closed its US$55 million Fund II in November 2022, doubling down on early-stage founders across Southeast Asia and South Asia. The firm operates with a strong conviction in AI-native companies and has been a consistent co-investor alongside Monk’s Hill Ventures, Accelerating Asia, and other regional funds.

Iterative’s portfolio includes companies building AI-powered financial services, B2B marketplaces, and enterprise software tools. The firm is particularly active in Vietnam, Indonesia, Bangladesh, and Singapore, and has backed multiple companies that have gone on to raise Series A rounds from top-tier global investors.

Notable AI investments & activity: Co-invested with Monk’s Hill in 1Long (AI wealth management, Vietnam, 2024). Co-invested with Accelerating Asia in PriyoShop (AI-powered B2B retail marketplace, 2024). Backed Opilot (AI copilot startup, Singapore, 2024). US$55M Fund II closed to double down on seed-stage AI founders (2022).

9. Singtel Innov8

Singtel Innov8 is the corporate venture capital arm of Singtel, Southeast Asia’s largest telecommunications group. With a mandate to invest in startups that complement and extend Singtel’s core business, Innov8 has backed companies across AI, cybersecurity, cloud computing, IoT, and digital media. In August 2022, Singtel Innov8 received an additional US$100 million to back startups in Southeast Asia, the US, China, Israel, and Australia. The fund’s AI investments span enterprise AI infrastructure, AI-powered connectivity solutions, and AI-native applications that leverage Singtel’s regional network and enterprise customer base.

Notable AI investments & activity: Joined Airalo’s US$60M Series B round (AI-powered eSIM platform, 2023). Backed Handprint (AI-powered impact measurement, 2022). US$100M additional capital deployed across AI, cybersecurity, and cloud startups globally. Active in AI & deep tech, advertising & marketing, and enterprise software.

10. Tin Men Capital

Tin Men Capital is a Singapore-based VC firm dedicated to backing B2B tech founders across Southeast Asia through capital, strategic connections, and operational resources. The firm focuses on Series A and Series B investments in B2B technology and marketplace startups, with a growing emphasis on AI-driven solutions for traditional industries.

In June 2026, Tin Men Capital published a detailed investment thesis on where it sees the greatest opportunities for AI-powered operational improvement in sectors such as logistics, manufacturing, and professional services, underscoring the firm’s conviction that AI will transform Southeast Asia’s most entrenched industries.

Also Read: Solving operational problems in traditional industries: Where Tin Men Capital sees opportunities for impact

Notable AI investments & activity: Published investment thesis on AI-powered operational improvement in traditional industries (June 2026). Active in B2B AI, marketplace AI, and enterprise software. Focused on Series A and Series B AI-native B2B companies across SEA.

11. Kadan Capital

Kadan Capital is a Singapore-based early-stage venture capital firm founded by Rei Murakami, daughter of renowned Japanese activist investor Yoshiaki Murakami. Launched in September 2024, Kadan Capital has quickly established itself as an AI-native VC firm with a focus on backing founders building AI-powered companies across Southeast Asia and Japan. The firm’s investment philosophy centres on identifying AI-first companies that can create durable competitive advantages in SEA’s fragmented markets. Kadan Capital has been vocal about the structural challenges facing SEA’s venture ecosystem, particularly the lack of exit opportunities, and positions itself as a long-term partner for AI founders navigating these headwinds.

Notable AI investments & activity: AI-native VC firm with explicit focus on AI-powered startups in SEA and Japan. Rei Murakami commented on the structural challenges for AI exits in SEA (Feb 2025). Active in early-stage AI investments across Singapore and the broader SEA region.

12. East Ventures

East Ventures is one of Southeast Asia’s most prolific venture capital firms, with over 644 investments since its founding in 2009. Headquartered in Indonesia and Singapore, the firm operates across the full investment spectrum from pre-seed to growth stage, and has backed some of the region’s most iconic companies including Tokopedia, Traveloka, and Ruangguru.

In January 2025, East Ventures predicted a ‘significant surge’ in AI-first startups across SEA, and in February 2025, the firm secured the first close of a US$100 million cross-border fund with SV Investment. East Ventures’s annual Digital Competitiveness Index for Indonesia is one of the most widely cited reports on the country’s digital economy and AI adoption landscape.

Notable AI investments & activity: Backed Videotto (AI-native video editing, Singapore, 2025). Predicted ‘significant surge’ in AI-first startups in SEA (Jan 2025). US$100M cross-border fund with SV Investment (Feb 2025). Annual Digital Competitiveness Index tracks AI adoption across Indonesia. Launched IndoBuild AI Demo Day in Jakarta (Mar 2025).

13. AC Ventures

AC Ventures is a leading Indonesia-based VC investor that has established itself as one of the most active investors in the country’s tech ecosystem. The firm focuses on fintech, AI-native software, consumer technology, and mobility, and has backed companies that have gone on to become category leaders in Indonesia and across Southeast Asia. AC Ventures is known for its deep operational expertise in Indonesia’s market dynamics and its ability to support founders navigating the country’s complex regulatory and consumer landscape. The firm has been particularly active in tracking and investing in AI-powered mobility and fintech companies, and has published widely read analysis on consolidation trends in SEA’s mobility sector.

Notable AI investments & activity: Portfolio includes Beam Mobility and ION Mobility (AI-powered micro-mobility). Published analysis on AI-driven consolidation in SEA mobility sector (Jul 2025). Active in AI-native software, fintech AI, and consumer AI across Indonesia and SEA.

14. Alpha JWC Ventures

Alpha JWC Ventures is one of a prominent venture capital firm, with US$700 million in assets under management and a decade of investing in Indonesia and the broader region. Founded in 2015, the firm has built a portfolio of over 60 companies across AI, fintech, consumer technology, and healthcare, and has backed multiple unicorns and category leaders. Alpha JWC’s investment thesis centres on the ‘Indonesia+ angle’, backing companies that can win in Indonesia’s large, complex market and then scale across SEA. The firm has been increasingly active in AI investments, backing companies that use AI to transform financial services, healthcare, and enterprise operations.

Notable AI investments & activity: Backed Honest (AI-powered credit card issuer, US$100M raised, 2025). Launched SpeakUp (AI-powered whistleblowing platform for startups, 2025). Led pre-Series A round for Bumame (AI-powered healthtech, 2025). US$700M AUM with growing AI portfolio across Indonesia and SEA.

15. Gobi Partners

Gobi is an Asia-focused venture capital firm headquartered in Kuala Lumpur and Hong Kong, with US$1.6 billion in assets under management. Founded in 2002, Gobi has built one of the most geographically diverse portfolios in Asia, spanning Malaysia, Singapore, Indonesia, the Philippines, Pakistan, and Japan. The firm has made AI, robotics, and biotech a central pillar of its investment strategy, marking its first healthcare AI investment in Southeast Asia in 2024.

In July 2026, Gobi entered into a strategic collaboration with NTT to connect Japan’s tech sector with SEA startups, and in November 2025, the firm expanded into Japan as a Global Network Partner. Gobi also backed SkyeChip, a Malaysian AI chip design startup, in early 2025.

Notable AI investments & activity: Invested in SkyeChip (AI chip design, Malaysia, 2025). First healthcare AI investment in SEA (2024). Strategic collaboration with NTT for Japan-SEA AI co-investments (Jul 2026). AI, robotics, and biotech are core investment pillars. US$1.6B AUM deployed across Asia with growing AI focus.

16. Insignia Ventures Partners

Insignia is a leading growth-stage venture capital firm, with over US$516 million raised across its funds. Founded in 2017, the firm has invested in over 90 companies spanning fintech, e-commerce, healthcare, and SaaS, and manages capital on behalf of premier institutional investors including sovereign wealth funds, university endowments, and family offices from Asia, Europe, and North America. Insignia’s founder-first approach and deep regional network have made it a preferred partner for AI-native founders seeking growth-stage capital in Southeast Asia. The firm has been bullish on AI, web3, climate tech, and healthcare as the defining investment themes of the decade.

Notable AI investments & activity: Backed Carro (AI-powered auto platform), Ajaib (AI-driven investment platform), and Payfazz (AI-powered financial services). Raised US$516M with explicit bullishness on AI, web3, climate tech, and healthcare (2022). Backed Konvy (AI-powered beauty e-commerce, Thailand, 2022).

17. Kickstart Ventures

Kickstart Ventures is the Philippines’s largest technology venture capital fund, connecting global innovation with Southeast Asia’s leading conglomerates and the markets they serve. The firm invests globally in early-to-growth-stage tech startups, with a particular focus on companies that can deliver strategic value and financial returns to its corporate limited partners.

In 2026, Kickstart published a widely read analysis on how AI is recalibrating venture capital in Southeast Asia, positioning the firm as a thought leader on the intersection of AI and VC in the Philippines and the broader region. Kickstart has been a consistent presence at Echelon Philippines, where it has shared its investment thesis on AI-driven transformation.

Notable AI investments & activity: Published ‘Recalibrating Venture Capital in Southeast Asia with AI’ (Apr 2026). Presented AI investment thesis at Echelon Philippines 2024. Philippines’ largest tech VC fund with growing AI portfolio. Connects global AI innovation with Philippine conglomerates and markets.

18. Intudo Ventures

Intudo Ventures is a firm with a distinctive ‘Indonesia-only’ investment mandate, backed by the conviction that Indonesia’s US$1.3 trillion economy and 270 million population represent one of the world’s most compelling standalone investment opportunities.

In November 2024, Intudo closed US$125 million across two funds focused on Indonesia’s middle-class and sustainable industry. The firm has been vocal about why it believes treating Southeast Asia as a single cohesive market is a fallacy, and has built a portfolio of companies that are deeply embedded in Indonesia’s consumer and enterprise landscape. Intudo’s AI investments focus on companies using artificial intelligence to serve Indonesia’s rapidly growing middle class.

Also Read: Why ‘Indonesia-only’ Intudo Ventures believes SEA as one cohesive market is a fallacy

Notable AI investments & activity: US$125M across 2 funds focused on Indonesia’s middle-class and sustainable industry (Nov 2024). Backed Banyu (AI-powered seaweed value chain, Jan 2025). Backed Coldspace (AI-powered cold chain logistics, 2023). Active in AI-driven consumer and enterprise solutions for Indonesia’s middle class.

19. Do Ventures

Do Ventures is Vietnam’s leading homegrown venture capital firm, with a mission to support tech startup companies in Vietnam and Southeast Asia. The firm provides finance, mentorship, and strategic connections to founders building technology companies that address the needs of Vietnam’s rapidly growing digital economy. Do Ventures has been at the forefront of tracking Vietnam’s AI investment surge, the country’s AI startup funding rose 13-fold to US$130 million in 2025, according to Do Ventures’ own research. The firm has backed companies across fintech, enterprise software, and AI-native applications, and has positioned itself as the go-to early-stage partner for Vietnamese founders building with AI.

Notable AI investments & activity: Published Vietnam Innovation and Private Capital Report 2025, documenting AI startup investment surge from US$10M (2023) to US$130M (2025), a 13-fold increase. Backed FlexOS (AI-powered hybrid work platform, 2022). Active in AI-native fintech, enterprise software, and consumer AI in Vietnam and SEA.

20. 500 Global

500 Global (formerly 500 Startups) is one of the world’s most active early-stage venture capital firms, with a significant regional presence in Southeast Asia anchored in Singapore. The firm has supported over 5,000 founders across more than 2,600 companies in 80 countries, including 51 unicorns. In September 2023, 500 Global raised US$143 million for early-stage and growth vehicles in SEA. In June 2024, the firm doubled down on AI, announcing a strategy to back startups building AI applications for specific industry verticals, reflecting a deliberate shift from horizontal AI tools to vertical AI solutions. 500 Global has been a consistent presence at Echelon Philippines and other regional conferences, where it has shared its AI investment thesis.

Notable AI investments & activity: Doubled down on AI apps for specific industry verticals (Jun 2024). US$143M SEA fund raised (Sep 2023). Backed NexMind (AI-powered multilingual digital marketing, 2023). Backed Canva (AI-powered design platform), Grab, and Udemy. Portfolio includes 51 unicorns with growing AI cohort.

The post Inside SEA’s AI gold rush: The 20 investors writing the biggest cheques appeared first on e27.

Malaysia’s public-sector pension fund, Kumpulan Wang Persaraan (Diperbadankan), or KWAP, has moved to contain concerns over its exposure to eFishery, saying its total investment in the troubled Indonesian aquaculture startup amounted to about US$38.4 million and represented a 2.51 per cent stake in the company.

The clarification comes after media scrutiny of eFishery, once one of Southeast Asia’s most closely watched agritech startups, following revelations of financial manipulation and misrepresentation in its accounts.

Also Read: “There’s no excuse”: Aqua-Spark calls out eFishery’s deception

eFishery co-founder and former CEO Gibran Huzaifah was recently sentenced by the Bandung District Court to nine years in prison after being convicted of embezzlement and money laundering.

The fallout is significant not only because eFishery was a flagship Indonesian startup but also because its cap table included several institutional investors. KWAP said it was a minority shareholder, while most of the company’s shares were held by other investors, including major global institutions that were also affected by the misconduct.

A pension fund caught in a startup blow-up

KWAP manages Malaysia’s public-sector retirement fund and invests across equities, fixed income, money market instruments, and private-market assets. The fund said that, after irregularities at eFishery were discovered, it conducted an internal investigation and reviewed its investment processes, post-investment monitoring arrangements, and the information available to it during the investment period.

“Appropriate follow-up actions have been taken in accordance with KWAP’s internal governance and accountability framework,” the fund said, adding that it is pursuing all available avenues to maximise recovery of its investment.

KWAP did not specify how much of the US$38.4 million investment it expects to recover, nor did it name the other affected institutional investors. It also did not disclose whether any legal action has been initiated by the fund.

The size of the exposure appears modest relative to KWAP’s overall balance sheet. Based on unaudited results for the financial year ended 31 December 2025, the fund recorded gross investment income of about US$1.96 billion and total funds under management of roughly US$45.9 billion. Still, the eFishery case raises uncomfortable questions for institutional investors that increased allocations to private markets during the region’s low-interest-rate venture boom.

eFishery’s fall from startup darling status

Founded in 2013, eFishery built its business around smart feeding devices for fish and shrimp farmers, alongside financing and marketplace services. It was part of a broader wave of Southeast Asian agritech startups seeking to formalise fragmented supply chains, digitise smallholder farmers and connect producers with credit and buyers.

The company gained prominence because aquaculture is a large and strategically important sector in Indonesia, the world’s largest archipelago and one of the biggest fish-producing nations globally. Indonesia’s fishery and aquaculture economy supports millions of livelihoods, but the industry has long been dogged by inefficiencies, opaque middlemen networks, limited working capital, disease risks and thin farmer margins.

That made eFishery’s pitch attractive: data-led feeding systems, farmer financing, procurement and distribution could, in theory, improve yields and reduce waste. For investors, the company offered exposure to a sector sitting at the intersection of food security, fintech, climate resilience and rural digitisation.

Also Read: eFishery founder held by Indonesian police over alleged embezzlement

The company’s collapse in credibility is therefore a blow beyond one balance sheet. Southeast Asia’s agritech sector has already had to contend with a tougher funding environment since 2022, as investors moved away from growth-at-all-costs models and demanded clearer paths to profitability. A fraud case at a high-profile startup will almost certainly sharpen scrutiny of revenue quality, customer verification, loan-book exposure and related-party transactions across the sector.

Regional peers face a different investor climate

eFishery operated in a market with several regional peers trying to solve different parts of the aquaculture and fisheries stack. In Indonesia, JALA Tech focuses on shrimp farm management and monitoring tools, helping farmers track water quality and production data. Delos has built a technology and operational platform for shrimp farming, including farm design and productivity improvement. Aruna, another Indonesian startup, works on fisheries commerce by connecting fishers with domestic and export markets. FishLog has focused on cold-chain and fisheries distribution infrastructure.

The eFishery affair may benefit more conservative operators if investors begin rewarding slower, verifiable growth over aggressive expansion. But it may also make fundraising harder for the entire category, particularly for startups whose business models mix hardware deployment, farmer credit and marketplace revenue, areas where field-level verification can be expensive and messy.

KWAP tightens private-market approach

In its statement, KWAP said it has strengthened its private-market investment approach, including greater portfolio diversification, investing alongside experienced fund managers and strategic partners, enhanced post-investment monitoring, and closer oversight of material developments involving portfolio companies.

Those measures reflect a broader reassessment among Southeast Asian limited partners, sovereign funds and pension funds after the exuberant funding cycle of 2020 to 2022. During that period, global capital flooded into the region’s startups, pushing valuations higher across fintech, e-commerce, logistics, Web3 and agritech. As liquidity dried up, weak governance, inflated metrics and fragile unit economics became harder to hide.

For pension funds, the challenge is especially sensitive. Private-market investments can improve long-term returns and diversify portfolios, but failures involving fraud or misrepresentation carry reputational and political consequences. The ultimate beneficiaries are retirees, not venture capital partners.

Also Read: 10 years behind bars? eFishery case forces startup reality check

KWAP stressed that its broader fund remains diversified across asset classes, sectors and geographies, and said it remains committed to managing the fund prudently and transparently in line with its statutory mandate to help the Malaysian government meet pension obligations to public-sector retirees.

The eFishery case is unlikely to end with one clarification. For Southeast Asia’s startup ecosystem, it is another reminder that governance is not back-office plumbing. In private markets, where valuations often depend on company-reported numbers and investor trust, governance can be the difference between a breakout story and a costly write-off.

The post Malaysian pension fund KWAP moves to contain damage after eFishery fraud shock appeared first on e27.

Japanese financial services group SBI Holdings has acquired a majority stake in Singapore-based crypto exchange Coinhako, turning the company into a consolidated subsidiary after receiving approval from the market regulator Monetary Authority of Singapore (MAS).

The transaction, completed on July 16 through SBI Ventures Asset, involves both a capital injection into Holdbuild, Coinhako’s parent company, and a share purchase from existing shareholders.

Financial terms were not disclosed.

Also Read: Regulation crypto is here: The 400-page rule that could kill or save American crypto innovation

The deal gives SBI a regulated foothold in one of Asia’s most closely watched digital asset markets at a time when crypto exchanges, stablecoin issuers, and tokenisation platforms are moving from retail-led speculation towards institution-facing infrastructure. On the other hand, Coinhako gets a deep-pocketed parent with a large financial services network in Japan, a market where SBI has been one of the most aggressive incumbents in crypto, blockchain, and digital securities.

Founded in 2014 by Yusho Liu and Gerry Eng, Coinhako operates mainly through Hako Technology, which holds a Major Payment Institution licence from MAS, and Alpha Hako, a crypto asset service provider registered with the British Virgin Islands Financial Services Commission.

Coinhako is among the island nation’s earlier consumer-facing digital asset platforms and has survived multiple industry cycles, including the post-FTX regulatory tightening that pushed many exchanges out of the market.

A Singapore bet, not just a Coinhako deal

For SBI, the acquisition is less about buying a standalone exchange and more about securing a regulated bridge into Southeast Asia.

The Japanese group said Singapore is a key hub in its digital asset strategy, particularly as it works to build what it describes as a digital asset economic zone focused on Asia-Pacific. SBI has also been working with Startale on on-chain financial infrastructure, including JPYSC, billed by the company as Japan’s first trust-type yen-denominated stablecoin.

SBI Chairman, President and CEO Yoshitaka Kitao said the group aims to create a “global corridor for digital assets” by connecting exchanges across markets. Singapore, he added, plays a central role because of its regulatory position.

That framing makes sense. Singapore has spent the past few years trying to separate regulated digital asset activity from the excesses of the last crypto bull run. MAS has tightened retail access, introduced stronger requirements around custody and customer asset segregation, and pushed licensed players towards compliance-heavy operations. At the same time, it has encouraged institutional experimentation in tokenisation, stablecoins and cross-border settlement through projects such as Project Guardian.

Also Read: The future of stablecoin payments will be decided in emerging markets

This has created a market where the cost of compliance is high, but the regulatory signal is clearer than in much of the region. For a Japanese financial group looking to expand digital asset rails outside its home market, acquiring a licensed Singapore operator is faster than building from scratch.

Coinhako gets scale after a brutal market cycle

For Coinhako, SBI’s backing comes after a period in which many regional crypto firms have struggled to maintain momentum.

Southeast Asia was one of the most active crypto retail markets during the last bull cycle, driven by young populations, high mobile penetration and underdeveloped investment infrastructure in several countries. But the sector has since split sharply. Regulated platforms in Singapore, Indonesia, Thailand, and the Philippines have continued to operate under tighter rules, while weaker or offshore-led players have faded, frozen withdrawals or been forced into restructuring.

Coinhako now competes in Singapore against global and regional names including Coinbase, Crypto.com, Independent Reserve, Gemini, and OKX — all of which have pursued regulatory approval in the city-state to varying degrees. In the wider region, competition includes Indonesia’s Indodax and Tokocrypto, the latter backed by Binance; Coins.ph and PDAX in the Philippines; and Bitkub in Thailand. Several of these players have stronger domestic retail recognition in their home markets but lack the same Singapore regulatory positioning.

The exchange’s challenge has been familiar: surviving long enough to become relevant to the next phase of the market. Retail trading fees alone are no longer a compelling growth story. The bigger opportunity now sits around compliant custody, tokenised real-world assets, stablecoin settlement, cross-border payment corridors and institutional digital asset access.

“Joining SBI Group is the natural next chapter for Coinhako,” said Liu, Coinhako’s co-founder and CEO. He said the platform had spent the past decade building in “one of the world’s most progressive regulatory environments” and would use SBI’s scale to deliver new digital financial services across the region.

Stablecoins and tokenisation are the real prize

The most important clue in the announcement is not the acquisition itself, but SBI’s repeated reference to JPYSC and cross-border digital finance.

Stablecoins have moved from a crypto trading utility to one of the most closely watched pieces of payments infrastructure in Asia. Dollar-linked stablecoins dominate global usage, but regulators and banks across the region are exploring domestic currency-backed tokens for settlement, treasury management and tokenised asset transactions.

Singapore has already established a regulatory framework for single-currency stablecoins, initially covering tokens pegged to the Singapore dollar or G10 currencies issued in Singapore. Japan, meanwhile, has taken a more bank-and-trust-led route, creating a path for regulated yen-denominated stablecoins. SBI’s attempt to connect these developments through Singapore could position Coinhako as more than a retail exchange.

Also Read: Stablecoins surge in Southeast Asia 2026: A real shift or just a bridge to CBDCs?

The same logic applies to tokenisation. Financial institutions in Singapore, Japan and Hong Kong have been testing tokenised bonds, funds, deposits and foreign exchange settlement. The problem is no longer whether assets can be tokenised; it is whether distribution, compliance, liquidity and settlement can be stitched together across jurisdictions.

A licensed Singapore platform with an existing customer base and operational experience may give SBI a local testbed for these services. It could also help the group connect Japanese digital finance infrastructure with Southeast Asian users and institutions, though that ambition will depend heavily on regulatory approvals in each market.

Japan-Singapore ties add political timing

The announcement also lands during the 60th anniversary year of diplomatic relations between Japan and Singapore. SBI said it plans to hold its first overseas branch managers’ meeting in Singapore this summer, signalling that the city-state is becoming more than a regional office for the group.

The broader backdrop is a growing convergence between Japanese capital and Southeast Asian fintech infrastructure. Japanese banks, trading houses, and financial groups have been active investors in regional payments, digital lending and wealth platforms. SBI’s Coinhako move extends that pattern into regulated digital assets.

Still, execution will be difficult. Crypto regulation in Southeast Asia remains fragmented. Singapore is strict but clear; Indonesia has shifted oversight from commodities regulators towards financial authorities; Thailand has allowed licensed exchanges but imposed advertising and product restrictions; the Philippines remains active but cautious. A “corridor” strategy will require SBI and Coinhako to navigate each of these regimes rather than assume a single regional playbook.

The acquisition gives SBI a credible base in Singapore and gives Coinhako more institutional muscle. But the deal’s significance will be measured by what comes next: whether the pair can move beyond exchange trading into stablecoin settlement, tokenised assets and cross-border financial rails that regulators will actually permit. For now, SBI has bought itself a seat at Singapore’s digital asset table. The harder task is turning that seat into regional leverage.

The post SBI buys majority stake in Coinhako to deepen Singapore digital asset push appeared first on e27.

Singapore is often described as a small market. That is true in population terms, but misleading in almost every other way.

It is better understood as a compressed market.

Customers, investors, regulators, partners, talent, and competitors operate unusually close to one another. Information moves quickly. So does reputation.

In larger markets, a weak proposition may survive for years across separate cities or customer segments. In Singapore, the feedback tends to arrive much sooner.

This can make the market feel unforgiving. For founders who know how to listen, it is one of Singapore’s greatest advantages.

Density changes the game

Singapore’s startup ecosystem brings public institutions, multinational corporations, investors, universities, accelerators, and founders together within a remarkably small geography.

The country ranks second globally and first in Asia-Pacific in StartupBlink’s 2026 Innovators Business Environment Index. According to the Singapore Economic Development Board, 80 of the world’s top 100 technology companies have a presence here, with many using Singapore as a regional or global base.

For founders, this density reduces the distance between an idea and the people capable of testing, funding, regulating, or buying it. But proximity also raises expectations.

A poor customer experience does not remain isolated for long. An investor may know the company that rejected a pilot. A corporate buyer may speak to a former employee. A promising introduction may lead to three more, while a poorly handled one can quietly close several doors.

In Singapore, reputation is not simply a branding exercise; it’s more like operating infrastructure.

Also Read: Inside Singapore’s startup boom: The 21 firms investors can’t stop funding

Feedback arrives early

After working with thousands of startups and SMEs, I have noticed that founders sometimes misread Singapore’s speed of feedback.

When customers hesitate, they conclude that the market is too conservative. When a pilot does not convert, they assume local companies are too cautious. When growth slows, they point to the size of the domestic market.

Sometimes those explanations are valid. Often, the market is revealing something useful.

The proposition may not be specific enough. The proof may not be strong enough. The founder may be speaking to an interested user rather than the person who controls the budget. The product may solve a real problem without solving one urgent enough to earn funding.

Singapore compresses the time required for these weaknesses to surface. A founder who discovers a flawed assumption in three months is in a stronger position than one who spends two years scaling it.

Validation is not scale

The mistake is expecting Singapore to play every role.

It is an effective market for validation, partnerships, credibility, capital, and regional coordination. For many companies, however, it cannot provide the customer volume available in Indonesia, Vietnam, the Philippines, or Thailand.

Southeast Asia’s digital economy surpassed US$300 billion in gross merchandise value in 2025, according to the latest e-Conomy SEA report. That opportunity is spread across markets with different languages, regulations, price sensitivities, payment habits, and expectations of trust.

Singapore can provide a strong base. It cannot remove the need to localise.

The Singapore Business Federation’s 2025 internationalisation survey found that 84 per cent of internationalised Singapore businesses operate in ASEAN. Among businesses planning further expansion, 65 per cent intend to grow within the region.

This is an important distinction: Singapore may be where a company proves that its model works, but regional markets determine whether that model can adapt.

Assumptions do not travel well

APAC expansion rarely fails because a product suddenly stops functioning. It fails because assumptions travel further than evidence.

A company enters a new market with the same positioning, pricing, sales process, and customer experience. The team expects the formula that worked in Singapore to transfer intact. Then conversion slows.

Also Read: Singapore and Taiwan have a new window of opportunity, but will they seize it?

In one market, customers may expect to speak with someone before buying software. In another, the right local partner may matter more than a polished digital funnel. Procurement cycles, payment terms, hierarchy, and perceptions of foreign brands can vary significantly.

Localisation, therefore, is not simply translation; it’s more like the recalibration of trust.

Singapore helps by exposing founders to regional buyers, talent, investors, and partners early. But proximity to Southeast Asia should not be confused with understanding it.

Use compression deliberately

Founders can use Singapore’s compressed environment in four practical ways:

• Test the commercial argument. A successful pilot means little if no one will own the budget after it ends.
• Treat reputation as infrastructure. Delivery quality, communication, and follow-through compound quickly in a closely connected ecosystem.
• Design for regional expansion. Separate the features needed in Singapore from the languages, payment methods, onboarding models, and partnerships required elsewhere.
• Use rejection as market intelligence. Repeated objections are rarely random. They reveal problems with positioning, timing, trust, or value.

Small can be powerful

Singapore’s limited domestic market is a constraint. But constraints can improve companies when they force clarity early.

Founders here must think regionally, demonstrate credibility, and learn quickly. They operate in a market where feedback travels fast, and weak assumptions have fewer places to hide.

That does not make Singapore easy. It makes Singapore efficient.

The founders who benefit most are not those who treat the country as a smaller version of a larger market. They recognise it as a concentrated environment in which ideas, reputations, and opportunities move unusually quickly.

Singapore is not merely a market to conquer. Think of it as a pressure test.

Used well, that pressure can produce companies ready for much larger ground.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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For two decades, winning search meant one thing: rank on page one. That game hasn’t disappeared, but it has quietly become the smallest of three games being played for your customers’ attention. Ask Google a question today, and it increasingly answers before the first blue link, inside an AI Overview. Ask ChatGPT, Gemini, or Perplexity, and there is no page one at all — just an answer, with a handful of brands woven into it. Either yours is one of them, or the conversation moves on without you.

That is why marketers now juggle three acronyms instead of one: SEO, AEO, and GEO. They are not competing philosophies. There are three layers of the same new reality, and in 2026, a business serious about being discovered needs all of them.

Three games, one customer

• SEO — Search Engine Optimisation — is the discipline we all know: making your website visible in traditional engines like Google and Bing through keyword targeting, backlinks, technical health (speed, mobile experience, crawlability), and content people actually find useful. Its purpose has always been simple: bring visitors to your site.
• AEO — Answer Engine Optimisation — is about winning the moment when a single answer gets lifted out and served directly: a featured snippet, a voice assistant’s reply, a line in Google’s AI Overview. Here, ranking a page matters less than structuring one — concise answers near the top, clear headings, and demonstrated authority on the topic, so the machine can extract you cleanly.
• GEO — Generative Engine Optimisation — which you may also see labelled AI SEO or LLM optimisation- is the youngest of the three disciplines and, increasingly, the decisive one. This is the work of making sure generative tools — ChatGPT, Gemini, Perplexity, Claude — decide you are worth naming when they answer a question: citing you, quoting you, recommending you. Unlike AEO, there is no single result to win. What matters is whether the places these models learn from — your structured data, review platforms, directories, forums, knowledge bases — tell one consistent, accurate story about who you are. Done well, your brand lives inside the answer even when no one ever reaches your website.

A useful shorthand: SEO is about keywords and clicks, AEO is about context and the answer box, and GEO is about entities and the mention.

Also Read: AI and the crisis of recognition: Do we still see the human behind the words?

Why 2026 is the tipping point

Three shifts make this urgent rather than theoretical. Zero-click behaviour is becoming the default — users take the answer and leave, never reaching a website even when your content produced that answer. AI platforms concentrate attention on a handful of sources they trust per query, which turns citation into a winner-take-all contest. And queries themselves have changed shape: people no longer type “website design Singapore” — they ask, “which company builds affordable websites for a small F&B business in Singapore?” Engines reward content that speaks the way people now ask.

Southeast Asia feels this earlier and harder than most regions. Its consumers are mobile-first and among the fastest adopters of AI assistants, and Singapore in particular is a brutally competitive, English-language market where a single AI answer can settle a shortlist. There is a quieter risk too: regional brands are thinly represented in the sources these models learn from. If you are not deliberately feeding the engines accurate, consistent signals, they will describe your category through your competitors — or describe you wrongly.

The moment it bites

Picture the buyer you most want. An operations director at a mid-sized Singapore company opens an AI assistant and types: “Best providers for this in Singapore — mid-sized team, tight budget. Give me three options.” Ten seconds later, she has three names, each with a tidy justification. Yours is not among them.

Nothing in your dashboard will ever record this. There was no impression lost, no ranking to recover, no analytics trail. In the old game, you could at least watch yourself losing from page two. In this one, invisibility is silent.

The content flood — and why creativity becomes the moat

Faced with all this, the reflexive strategy is volume: use LLMs to generate hundreds of optimised articles and carpet-bomb every question in your category. Here is the uncomfortable arithmetic — everyone can now do that. When every competitor can generate a thousand plausible “ultimate guides” overnight, generated volume is worth precisely nothing. The web is filling with synthetic sameness, and both search engines and AI models are getting sharper at collapsing near-duplicates and discounting content that adds no new information. A model deciding what to cite behaves, in this one respect, like a tired editor: it keeps what is distinctive and skips the rest.

So the differentiators flip. What earns citations is what generic generation cannot produce: first-hand data nobody else has, a point of view sharp enough to be quotable, and creative angles into whitespace no competitor occupies. Across the markets, the pattern is consistent — categories converge on the same three messages, and the brand that finds the untouched angle is the one that gets remembered, by humans and machines alike. You cannot prompt your way into being the answer. You have to say something worth answering with.

Also Read: Bitcoin at US$64,660: The hidden on-chain signal that suggests we’re still in a bear market

None of this replaces the fundamentals, which are quickly summarised: answer the actual question in your first sixty words; structure pages with clear headings, FAQs, and schema markup; keep your brand’s facts (what you do, where you operate) identical everywhere they appear; build presence on the third-party sources AI reads — reviews, directories, industry publications; and start measuring mentions and citations, not just clicks.

Creativity with evidence, not instead of it

The honest objection is that originality is expensive. Research, ideation, and testing take weeks that most teams don’t have. It’s a challenge we’ve encountered firsthand at SOMIN, where we’ve explored how AI can help teams analyse competitor and audience data, identify gaps in a category, and evaluate creative concepts before significant resources are committed.

In our experience, this has helped reduce research and ideation time for some organisations, giving teams more space to focus on creative thinking rather than repetitive groundwork. The machine does the reading. The humans get their time back to do the daring.

The future belongs to brands worth citing

AEO and GEO are not the death of SEO — they stand on its shoulders, because AI systems still select and cite from well-indexed, well-structured, credible pages. The strategy for 2026 is integration: SEO for discoverability, AEO for the answer, GEO for the recommendation, and creativity as the thread that makes any of it worth surfacing.

So ask yourself the question your customers are already asking their assistants: when an AI describes your category next year, will it have anything distinctive to say about you — or will it quote whoever was brave enough to be original?

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post AI is answering your customers before they ever click, and it may never mention you appeared first on e27.