Every quarter, I sit down with the operating reviews of businesses that use my scaling framework. Over the last year, one pattern has become impossible to ignore. The AI tooling line in these reviews has grown fast. New copilots, new agents, new automation layers stacked on top of CRMs and ERPs that have not changed in years. But when I ask who owns the workflow that tool just automated, or what approval step disappeared because of it, the answer is usually silence. The tool got bought. The business did not get rebuilt.

That gap is the real story in Southeast Asia right now, and most of the coverage is missing it.

The adoption numbers are genuinely strong. Recent regional research puts nearly half of Southeast Asian companies past the pilot stage, ahead of the global average. Singapore and Indonesia are leading, with more than half their firms moving toward scaled deployment. Singapore’s SME adoption rate alone tripled in a year. Founders across the region report AI is now embedded across multiple parts of their business, not just one department running an experiment.

None of that is in dispute. What is in dispute is whether adoption is the same thing as scale. In my work, it rarely is.

Also Read: AI is making Southeast Asia’s startups faster, not richer, yet

Adoption is a purchase decision. Someone in finance or operations signs off on a tool, it gets rolled out to a team, usage numbers go up, and that gets reported as progress. Scale is a redesign decision. It means the approval chain shortens because the tool now makes the judgment call a person used to make. It means the org chart changes because a role that existed to catch errors is no longer needed at that step. Most Southeast Asian enterprises I see have done the first and skipped the second, and they are calling it transformation.

I saw this clearly in a logistics business I worked with earlier this year. They had automated document processing for vendor onboarding, cutting a five-day manual cycle down to a few hours. Impressive on paper. But the compliance review that sat downstream of that process was untouched. The team still routed every file through the old sign-off chain, because nobody had rebuilt the chain around the new speed. The business had adopted AI. It had not scaled around it. The bottleneck just moved.

This is where the regional data on barriers gets interesting. Talent shortages and integration debt are always cited as the top blockers, and they are real. But they are usually framed as an AI specialist problem: hire more data scientists, more ML engineers. In my experience, the actual shortage is different. It is a shortage of people who can look at a workflow, decide what should be removed rather than augmented, and rebuild the operating structure around a faster core. That is not a technical skill. It is a scaling skill, and it is far scarcer than the talent reports suggest.

Also Read: Asia’s AI race won’t be won by capital or talent, but by whoever can keep the lights on

Regulatory fragmentation across the region compounds this. A business scaling from Colombo into Jakarta and Ho Chi Minh City is not just deploying the same AI stack three times. Data residency rules differ, compliance timelines differ, and what counts as an acceptable automated decision differs by market. Businesses that treat AI as a single global rollout hit friction fast. Businesses that treat each market as a separate operating design, with AI as one input into that design, move faster precisely because they planned for the difference upfront.

The sectors furthest ahead prove the point. Financial services in Singapore and Indonesia are not just running fraud models; they have restructured underwriting teams around what the model now decides versus what a human still reviews. Manufacturing and logistics firms doing predictive maintenance well have changed shift planning and procurement cycles to match, not just installed sensors. The lesson is consistent. The businesses pulling ahead are not the ones with the most tools. They are the ones willing to tear down and rebuild the layer the tools sit on top of.

For founders reading this with product market fit already behind them, the question worth asking is not which AI tool to adopt next. It is which part of your current operating structure you are protecting out of habit rather than necessity. Southeast Asia’s AI adoption curve is real and it is not slowing down. But adoption without redesign just makes your old bottlenecks faster. Scale only shows up when you are willing to change what the business looks like, not just what it uses.

—

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 Southeast Asia doesn’t have an AI adoption problem, it has a scaling problem appeared first on e27.

In a world of hyper-optimisation, startups are accidentally engineering themselves into a corner. We use AI to remove variance, to predict customer behaviour, and to standardise our outputs. But as behavioural economics research from 2026 warns, once behaviour is modelled, it becomes a commodity. If your startup is perfectly predictable, it is perfectly replaceable.

The ultimate hack isn’t more data; it is the high-agency outlier.

The death of the average success

Most AI tools are built on a regression to the mean. They suggest the most likely successful path based on historical data. However, McKinsey’s State of Organisations 2026 reveals that while 88 per cent of firms are experimenting with AI, only 19 per cent are seeing Frontier results. The difference? The leaders in that 19 per cent aren’t just using AI to be efficient; they are using it to empower workers to do recombinant, novel work.

They are looking for the glitch, the human insight that contradicts the data but captures the cultural moment.

Hiring for systemic defiance

We have been trained to hire team players who follow the workflow. In 2026, you need to hire for Systemic Defiance. These are the individuals who understand your Project Architecture so deeply that they know exactly when to break the rules to achieve the intent.

• The reframe: Don’t look for people who are good at AI. Look for people who are good at ignoring AI when it matters most.

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

The software for one as a culture

The most dangerous (and valuable) talent in your organisation doesn’t wait for a roadmap. They are the ones building their own agentic workflows to bypass internal bureaucracy. Microsoft’s 2026 Work Trend Index shows that high-agency workers are increasingly using AI teammates to expand their personal output, effectively becoming a company of one within their firm.

• The strategy: Instead of standardising their tools, invest in their outliers. If a team member builds a custom vibe-based tool that works, don’t ask for a security audit first. Ask how it changes the game.

The conclusion: The wildcard moat

The startups that will define the next decade in Singapore aren’t the ones with the most efficient AI. They are the ones that have built a culture where Human Intelligence is allowed to be weird, defiant, and original.

In an age of manufactured certainty, the only way to win is to be the one thing the machine didn’t see coming.

—

The post The outlier advantage: Why your startup needs glitch talent appeared first on e27.

Thailand’s bid to become a regional centre for artificial intelligence is moving from policy ambition to ecosystem-building.

At the Huawei Thailand Digital & AI Summit 2026 in Bangkok, Huawei joined Thai customers, local large language model developers, AI associations and universities to launch the Thailand AI Ecosystem Initiative, a programme aimed at strengthening the country’s AI infrastructure, governance, talent base and industry adoption.

Also Read: Huawei Cloud bets on Thailand as enterprises move from AI pilots to production

The summit, co-hosted by Huawei and Thailand’s Ministry of Digital Economy and Society, was held on July 23 and 24 under the theme “Advancing All Intelligence Thailand”. It brought together more than 3,000 participants, including government representatives, telecom operators, enterprise customers and technology partners.

The initiative arrives at a time when Southeast Asian governments are trying to capture more value from AI, not only by encouraging companies to use the technology, but also by building the underlying foundations needed to develop, deploy and govern it locally. For Thailand, that means cloud and computing infrastructure, trained professionals, trusted data sets, industry-specific models and clearer rules on responsible use.

H.E. Chaichanok Chidchib, Thailand’s Minister of Digital Economy and Society, said the country’s AI development agenda rests on three pillars: infrastructure, trust and people.

“The future of AI cannot be created by any single organisation alone,” he said, adding that Thailand is positioning itself as a “real-world AI Governance Sandbox”, where companies, researchers and regulators can work together to turn high-level AI principles into practical rules and deployment models.

That framing is important. In Southeast Asia, AI policy often runs ahead of implementation. Governments are keen to promote innovation, but they also have to manage concerns around privacy, security, bias, labour disruption and foreign technology dependence. A sandbox approach gives Thailand a way to test governance models while still encouraging private-sector experimentation.

From AI pilots to industrial deployment

Huawei’s message at the summit was that Thailand is entering what the company calls the “Agentic AI era”. In simple terms, agentic AI refers to systems that do more than answer prompts. These AI agents can plan tasks, use software tools, remember context and take steps towards a goal with some degree of autonomy.

For businesses, the appeal is clear. A bank may use AI agents to assist with software development, document review or customer operations. A hospital could use them to support administrative workflows. A manufacturer might deploy them to analyse equipment data and recommend maintenance action. But moving from isolated pilots to real-world deployment is difficult, especially in markets where companies still face gaps in computing capacity, data readiness and specialised AI talent.

Austin Zheng, Deputy Managing Director of Huawei Thailand, said local industries face five main bottlenecks in AI implementation: computing resources, data, models, security and talent. To address these, Huawei introduced its ACT framework: assess high-value scenarios that can produce commercial outcomes; calibrate AI models with high-quality vertical data; and transform business operations by developing AI talent and speeding up application development.

The framework reflects a broader shift in enterprise AI. After the first wave of experimentation with generative AI tools, companies are asking harder questions: Which use cases are worth funding? Do we have the data to support them? Can the models understand our industry? How do we protect sensitive information? And who inside the organisation can maintain these systems once the vendor leaves?

For Thailand, these questions are especially relevant in sectors such as finance, telecoms, healthcare, logistics and public services, where AI could improve efficiency but where mistakes can carry real consequences.

A local ecosystem, not just imported technology

The Thailand AI Ecosystem Initiative is being positioned as a collaborative effort rather than a single-vendor programme. It was jointly proposed by Huawei, the AI Association of Thailand, telecom operators, local large model developers and universities.

Also Read: Agentic AI ambitions in Singapore run into legacy systems and data quality gaps

Its stated goal is to build an inclusive local AI ecosystem that balances innovation with trustworthy governance, while developing digital talent and helping Thailand become a core engine for AI innovation and industrial deployment in Southeast Asia.

The emphasis on local models and local talent is significant. Much of the global AI conversation is still dominated by US and Chinese technology giants, but Southeast Asian markets have their own languages, regulatory conditions and industry needs. AI systems trained mainly on English-language or foreign data may struggle with Thai-language context, local public-sector workflows, or industry-specific terminology used in domestic companies.

This is where universities, AI associations and local model developers become important. They can help create trusted local corpora — curated data sets that reflect the local language and operating environment — while training the next generation of engineers and AI practitioners.

Huawei said it will support talent development through its ASEAN Academy and continue building its local presence in Thailand. The company has operated in the country for 27 years and uses the slogan “In Thailand, For Thailand” to describe its local strategy.

Infrastructure as the new AI battleground

Behind the ecosystem language sits a more concrete issue: computing power.

Hong-Eng Koh, Global Chief Public Services Industry Scientist of Huawei’s Global Public Sector Business Unit, said AI is evolving from assistive tools into industrial agentic applications, creating heavy demand for frontier models and unified computing architecture. Huawei said its AI portfolio covers model iteration, trusted local corpora, AI agent development and talent training, supported by infrastructure such as the Atlas 950 SuperPoD for large-scale computing coordination.

The company said its AI solutions have supported more than 2,600 enterprises across over 30 industries worldwide, spanning more than 500 business scenarios in sectors including finance, transportation, manufacturing and healthcare.

These figures speak to Huawei’s global ambitions, but Thailand’s market will be shaped by local execution. Enterprises need reliable infrastructure, but they also need integration partners, compliance support and measurable returns. AI adoption in Southeast Asia is rarely a simple matter of buying the most advanced model; it is often about fitting technology into messy, existing systems.

Huawei’s rivals in Thailand’s AI race

Huawei is competing in a crowded field. Global cloud and AI players such as Amazon Web Services, Microsoft, Google Cloud and Oracle are investing heavily across Southeast Asia, while Alibaba Cloud and Tencent Cloud remain active among regional digital businesses and Chinese-linked enterprises. In AI infrastructure, Nvidia’s chips and software ecosystem remain central to many enterprise deployments, while local telecom operators and data centre providers are also positioning themselves as partners for sovereign cloud, edge computing and AI workloads.

This competition is likely to benefit Thai enterprises by widening their options, but it also raises strategic questions for policymakers and companies. As AI becomes critical infrastructure, decisions about vendors, data location, compute supply and model governance will carry long-term implications.

For Huawei, the Thailand AI Ecosystem Initiative is both a policy-aligned partnership and a market-building move. It ties the company more closely to Thailand’s national AI agenda while giving it a role in infrastructure, applications, governance and training.

For Thailand, the test will be whether such initiatives can produce more than summit-stage announcements. Becoming ASEAN’s leading AI hub will require not only partnerships with large technology companies, but also stronger local research, industry adoption, startup participation, interoperable standards and rules that earn public trust.

Also Read: The coming identity crisis of agentic AI

The country has many of the ingredients: a sizeable domestic market, a strategic location in mainland Southeast Asia, active telecom and banking sectors, and a government eager to push digital transformation. The next step is turning those ingredients into deployable AI systems that solve real problems for citizens and businesses.

Huawei’s initiative adds momentum to that effort. Whether Thailand can convert it into regional leadership will depend on how quickly its ecosystem can move from ambition to execution.

The post Huawei launches Thailand AI Ecosystem Initiative to support ASEAN hub ambition appeared first on e27.

A founder is a few minutes into an investor pitch. “SEA expansion, Q3,” he says, and moves on to the next line.

The investor stops him. “Which part of SEA?”

He hadn’t decided. Half a dozen countries, several language groups, a handful of regulators at very different levels of maturity. He’d said “SEA” the way you’d say a country. It isn’t one.

Nobody in that room needed reminding of that, and yet there it was.

Anyone who has raised money, or pitched an APAC expansion to a board, has been there.

One word, many markets

What went wrong wasn’t preparation exactly. It was treating several markets like one, which is an easy trap and a common one. “Southeast Asia” gets said as though it’s a single buyer with a single set of rules. It isn’t. A dozen different buyer logics live under that word, several currencies, a spread of regulators who each move at their own pace and occasionally in opposite directions.

There’s a reason people fall into this. Most GTM playbooks were built for markets with one regulator and one dominant way of paying for things. Drop that playbook into SEA unchanged and the problem isn’t localisation, it’s that half the assumptions baked into the playbook were never designed to survive that many countries at once.

However, the mistake rarely lives where people go looking for it. The deck’s usually fine. The same goes for the pricing model, mostly. What’s actually broken is further down: a buyer in Singapore doesn’t decide the way a buyer in Hanoi does, and nobody’s stopped to check if that’s actually right. Leave it unchecked long enough and it starts costing real money.

Why this is getting more expensive

This particular mistake used to be a mild drag on conversion. It’s turning into something closer to real exposure, and that’s structural, not a bad quarter.

Trade relationships are being rewritten in real time. Decisions on AI infrastructure, on capital, on supply chains, are being made for political reasons nearly as often as economic ones now. Treat SEA as one internally consistent market in that climate, and you don’t just get a weaker GTM plan; you get a plan with nothing to say when one part of the region moves in a different direction to the rest of it.

Also Read: The 27 SEA biotech firms betting on cells, fermentation, and code

Which is roughly what’s happening to SEA’s role for a lot of companies. It’s stopped being purely a growth line and started being a hedge, a way of not having all your eggs in one geopolitical basket. That founder’s “expansion” and the word he actually needed, “hedge,” aren’t interchangeable. They come with different obligations attached.

US$235 billion. That’s what Southeast Asia pulled in FDI in 2024, more than China managed, and a fair chunk of it from firms trying to get some distance from the current geopolitical storms. Supply chains are telling the same story: more firms spreading into ASEAN without actually leaving China behind.

It’s not just a labelling issue either. Get an assumption wrong in a growth market, you lose some conversion, annoying but you’ll live. Get it wrong in a hedge and the whole thing stops working, because a hedge only earns its keep by behaving differently from whatever it’s protecting you from. Slap the word “hedge” onto a strategy that’s really just your US or China playbook copied over, and you haven’t hedged a thing. You’ve made the same bet twice and called it something smarter.

Singapore’s real role

We think of Singapore as a “regional hub,” but that sells it short. A hub is somewhere things pass through on the way to somewhere else, and that’s not really what’s happening here. The US and China are drifting further apart, and Singapore sits in the gap between them, still talking to them both.

You can see this playing out in three places right now.

• Banking. This is deliberate infrastructure, not something Singapore fell into. MAS named DBS as the country’s second RMB clearing bank in December 2025, adding another piece of China-facing capacity. Around the same time, capital nervous about US tariffs has been landing in Singapore for its stability. Few financial centres can genuinely hold both of those relationships at once, and that capacity is frequently the actual reason a cross-border deal clears.
• Regulation. MAS tends to move on data, AI risk, and digital assets before Indonesia, Vietnam, or the Philippines get round to it. Keep half an eye on Singapore, and you get a reasonable early read on where the rest of the region will eventually land. Not a certainty, but a decent lead indicator.
• Partnerships. Cross-bloc deals keep getting routed through a Singapore entity. People assume that’s about paperwork; it isn’t. Usually, the paperwork’s no easier. It’s the structure doing the work. Route a deal through a Singapore entity and both sides get a neutral jurisdiction to point to if anyone asks awkward questions later, something a direct US-to-China relationship can’t offer.

None of that makes Singapore neutral in the passive, staying-out-of-it sense. Singapore is actively earning its place at the table, and that’s a far more useful position than sitting on the fence.

Also Read: Hong Kong’s pitch to SEA: “We want to be your super partner”

Three things worth changing

If “we’ll work out the country-by-country detail later” is roughly where your SEA strategy currently sits, here’s what’s worth doing before the next pitch.

First, write down what you actually believe applies across the whole region. Pricing, buyer seniority, how long a sales cycle takes, whatever’s currently sitting there unexamined. Then test each belief country by country. Expect most of it to fall apart; that’s what the exercise is for.

Second, build the Singapore layer properly rather than letting it happen by accident. Most companies set it up on a lawyer’s advice and leave it there. A year later they realise it could have been doing real work all along; banking, early regulatory reads, structuring partnerships, if anyone had planned for that from day one.

And finally, if what you’re doing in SEA is actually a hedge, call it one instead of an expansion. The budgets are different. So is the risk tolerance you should be applying, and so is what counts as success. A market you’re hedging into earns its keep through resilience and optionality, not by hitting the same growth curve as your home market.

The pitch, rewritten

Back to that room. This time around, he says something closer to the truth: which country first, on what regulatory basis, hedged against what exactly, and it comes out sounding a lot less polished than “SEA expansion, Q3” did. Nobody pulls him up on it either. There isn’t really anywhere left to go.

Most SEA strategies aren’t wrong because nobody in the company is sharp enough to spot the problem. They’re wrong because nobody’s actually been made to test the assumption yet, not until an investor asks the awkward question, or a regulator forces the issue, or the pipeline quietly stalls and won’t say why.

So, what assumption is yours currently running on, that nobody in the room has actually tested?

—

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 SEA isn’t just a growth market anymore, it’s a hedge appeared first on e27.

For many startups in Southeast Asia, Europe appears attractive for obvious reasons. It offers affluent consumers, mature digital infrastructure, access to capital, strong demand for innovation and a large number of business customers looking for new technologies. From a distance, it can also look relatively simple: one continent, a shared regulatory framework in many areas, common payment standards and a single narrative built around expansion into “the European market”.

That perception is convenient, but commercially dangerous. Europe is not one market. It is a collection of countries with different languages, purchasing behaviours, levels of trust, expectations around service, approaches to risk, sales cycles and relationships with brands. Even neighbouring countries can respond very differently to the same offer, the same pricing structure or the same communication strategy. A startup that treats Europe as a single destination may therefore spend heavily on translation, acquisition and partnerships without ever understanding why its results remain inconsistent.

The first mistake is often strategic rather than operational. Companies decide to “launch in Europe” before choosing which specific European market they are actually prepared to understand. They build one website, translate it into several languages, run regional campaigns and assume that product-market fit will travel automatically. In reality, international expansion is not the reproduction of a domestic model across a larger territory. It is a sequence of local commercial decisions, each with its own constraints.

Europe shares rules, not customer behaviour

The European Union has created significant regulatory and economic integration, but regulation does not erase national market cultures. A company can comply with the same legal framework in France, Belgium and Luxembourg while facing completely different buying behaviours in each country. It can offer the same product in Germany and the United Kingdom, yet encounter different expectations regarding proof, pricing, onboarding and customer support.

France, for example, often requires a high degree of reassurance before a new provider is considered credible. Buyers may want detailed explanations, references, local language support and a clear demonstration that the company understands their environment. In the United Kingdom, the same audience may respond more quickly to a sharper commercial proposition, clearer differentiation and a direct explanation of return on investment. Switzerland can demand premium execution, precision and trust, while Belgium may require a more fragmented approach because linguistic and regional realities affect how companies communicate and decide.

Also Read: AI is making Southeast Asia’s startups faster, not richer, yet

These differences influence much more than marketing. They affect the sales process itself. The number of people involved in a decision, the acceptable level of risk, the importance of local partners, the preferred communication style and the pace of negotiation can all vary. A startup may interpret a slow response as lack of interest when the real issue is insufficient credibility. It may lower its price when the market was actually waiting for stronger proof. It may increase advertising when the problem lies in the structure of the offer.

Europe is unified enough to create the illusion of simplicity, but diverse enough to punish that illusion.

Translation cannot repair a weak market entry strategy

One of the most common shortcuts is to equate localisation with translation. A startup translates its website, advertisements and product interface, then assumes it has adapted its offer. This can make the company technically accessible while leaving it commercially irrelevant.

Translation changes the language of a message, but not necessarily its meaning in context. A promise centred on speed may work in one country and appear superficial in another. A highly informal brand voice may create proximity in one market and reduce credibility in another. A pricing page that feels transparent to one audience may appear incomplete elsewhere if buyers expect stronger guarantees, human support or more detailed contractual information.

The same problem applies to product packaging. European customers may differ in the way they evaluate subscriptions, free trials, annual commitments, implementation support or data protection. A model that performs well in Singapore may need a different level of explanation, onboarding or after-sales support in France. A product can remain technically identical while the commercial architecture around it must change.

Startups should therefore separate three questions that are too often mixed together: Is the product relevant? Is the offer understandable? Is the company credible? A market can show strong need for the product and still reject the company because the offer is poorly framed or because the startup has not built enough local trust. That distinction is essential, because otherwise teams may modify the product when the real weakness lies in positioning, distribution or communication.

The right entry point matters more than continental ambition

The most effective European expansion strategies usually begin with one market, not five. Choosing an entry country forces the company to make specific decisions. Which customers will be targeted first? Which problem will be emphasised? Which local proof is missing? Which channels are realistic? Which partnerships could reduce the cost of credibility?

The best entry market is not always the largest. It may be the one where the company already has a partner, where the founder’s network is strongest, where English can be used during the first phase, or where the competitive environment leaves a clearer position available. A smaller market can provide faster learning and more useful references than an ambitious launch across several countries at once.

Also Read: Why investors often back Vietnamese startups more aggressively than Thai peers

This does not mean that startups should abandon regional thinking. It means they should build it progressively. A successful first market creates evidence: customer feedback, local references, sales objections, onboarding data and a clearer understanding of what must change. These lessons can then influence the next market rather than forcing the company to repeat the same assumptions at greater cost.

The sequence also matters for brand development. If a startup enters several countries simultaneously, each local team may adapt the message independently, creating different versions of the company before its European identity has stabilised. Starting with one market allows the company to determine which parts of its positioning are fundamental and which can be adapted without creating inconsistency.

AI can accelerate adaptation, but not replace judgement

Artificial intelligence can significantly reduce the cost of preparing for European expansion. It can support market research, analyse customer reviews, compare competitors, identify recurring objections, generate alternative messages and accelerate multilingual content production. For a startup with limited resources, this creates genuine leverage.

The danger begins when AI is used as a substitute for local understanding. Models can summarise patterns, but they cannot automatically determine which differences are commercially meaningful. They may reproduce outdated assumptions, flatten cultural nuance or generate recommendations that sound plausible without reflecting how buyers actually behave. A startup that relies only on AI can produce sophisticated localisation at high speed while remaining disconnected from the market.

The strongest use of AI is therefore iterative. Teams can use it to create hypotheses, prepare interviews, compare market narratives and structure large volumes of information. Those hypotheses must then be tested with customers, local advisors, partners and sales conversations. The purpose of the technology is not to eliminate human judgement, but to make learning faster and more systematic.

Startups should also avoid using AI to multiply content before clarifying their European positioning. Producing ten localised campaigns is not progress if the underlying value proposition remains vague. Technology should amplify a strategy that is already coherent, not conceal the absence of one.

For Southeast Asian startups, Europe can still be an exceptional growth opportunity. But the continent rewards precision more than scale at the beginning. The companies most likely to succeed will be those that stop asking how to enter Europe and start asking which European market they are ready to understand first.

—

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 Why Southeast Asian startups should stop treating Europe as one market appeared first on e27.