Bitcoin currently faces a highly complex market environment that puzzles many seasoned investors. The premier digital asset struggles to break above the US$70,000 price level last observed in May. A closer look at market indicators reveals deep underlying weakness despite favourable macroeconomic conditions.

The Coinbase Bitcoin Premium Index calculates the price difference between Bitcoin on Coinbase and Binance to estimate buying or selling pressure from the United States market. This specific index has stayed negative for 90 consecutive days. At the time of writing, the index stood at -0.1066 per cent.

A negative reading indicates that the asset trades at a lower price on Coinbase than on Binance. This persistent discount highlights a profound lack of domestic buying interest. This prolonged negative premium is a glaring warning sign for the broader cryptocurrency sector. Smart money clearly anticipates further downside risk and refuses to accumulate more digital assets at current valuations.

This steep decline occurred while broader financial markets celebrated new record highs. The Relative Strength Index remained largely below the neutral level, reflecting prevailing bearish sentiment. Bollinger Bands further supported the volatility that prevented the price from hitting a high bullish threshold. Even massive accumulation by large holders failed to reverse the downward trend.

Whale wallets bought 54,000 more coins since mid-June, but the price action ignored this aggressive accumulation. Buy-side support below the current price continues to erode rapidly. A significant concentration of buy orders below the market existed earlier, especially in June. This created a solid floor because buyers were prepared to absorb selling pressure if the asset dropped toward those levels.

Market participants have now removed or shifted many of those bids lower, leaving fewer orders directly beneath the price. The market liquidity buffer has weakened significantly with less buy-side support to cushion further declines. I consider this lack of underlying bid depth a major structural vulnerability. Order book dynamics clearly show that large players are stepping away from defending current valuation levels.

Also Read: Pokemon cards gained 22.8% while Bitcoin lost 20.7% and that gap should worry every investor

The digital currency had nearly everything going its way this week but remains on track to finish roughly three per cent lower. This divergence strikes a particularly discordant note, given the asset’s reputation as a high-beta proxy for technology stocks. Wall Street pushed to fresh record highs as inflation cools and traders dial back expectations for a Federal Reserve rate hike in September.

These conditions normally favour speculative assets. The digital currency fell from around US$65,000 on Monday to US$62,470 by Friday. The tech-heavy Nasdaq 100 closed the week approximately one per cent higher during the exact same period.

Last week produced a clean dovish signal, combining cooler inflation with a weakening labour market, as reflected in favourable producer price index and jobless claims data. This refusal to follow traditional equities is deeply concerning for momentum traders. This distinct decoupling suggests that internal market mechanics currently overpower external macroeconomic stimuli. The asset faces its own distinct demand problem, setting it apart from the broader stock market rally.

Michael Saylor serves as the executive chairman at Strategy, which holds the record as the largest public company holding this asset. He offered the clearest explanation for this divergence earlier this month. Saylor noted that an enormous amount of capital is currently flowing into artificial intelligence infrastructure. Companies such as Alphabet, Meta, and SpaceX represent the largest near-term headwinds for the digital currency.

The premier cryptocurrency and artificial intelligence currently compete for the exact same speculative and institutional capital. Artificial intelligence is winning this battle for investor attention right now. This massive capital rotation explains why the digital currency refuses to participate in the broader equity rally. I believe this technological distraction will continue suppressing digital asset prices until the artificial intelligence hype cycle naturally cools down.

Institutional investors simply prefer the tangible revenue growth of technology giants over the speculative store-of-value proposition during uncertain economic times. This sector rotation severely limits the liquidity available to alternative assets seeking robust capital inflows. Wall Street allocates billions to data centres rather than decentralised ledger networks.

Also Read: Is the US$63,750 line the only thing standing between Bitcoin and US$62,000?

Exchange-traded funds further illustrate this lack of institutional enthusiasm. United States spot exchange-traded funds recorded US$5.48 billion in net outflows in 2026. These funds have only recovered US$459.6 million so far in August, as of August 14. This massive capital exodus confirms that large funds are reducing their exposure.

The digital currency formed a smaller bear pennant around US$60,000 to US$65,000 since the June selloff. This formation represents another bearish continuation pattern that technical analysts monitor closely. A decisive break below the rising support of this pennant could accelerate the existing flag breakdown. The measured move points toward approximately US$46,300.

That calculation puts the broader downside target zone at roughly US$45,000 to US$52,000. I expect the market to test these lower support levels before finding any meaningful long-term stability. Traders must respect these technical breakdown signals and adjust their risk management strategies to protect their portfolios from sudden drawdowns. Chart patterns rarely lie, and this specific setup screams further downside action for anyone paying close attention.

The broader economic backdrop adds another layer of complexity to this situation. The United States national debt currently nears US$40T. This massive fiscal burden forces the government to issue more bonds, which drains liquidity from the financial system.

I argue that this expanding debt ceiling inherently restricts the amount of excess capital available for highly speculative assets. The combination of massive artificial intelligence investments and soaring national debt creates a perfect storm that suppresses digital asset valuations. Investors must recognise that the digital currency no longer moves in lockstep with traditional risk assets.

Market participants should prepare for increased volatility and potentially lower prices in the coming weeks. Prudent traders will likely hedge their portfolios against these impending macroeconomic shocks. The digital asset must overcome these significant structural headwinds before it can resume its historical upward trajectory. Careful observation of order book depth will provide the next major clue. Global liquidity constraints will dictate the next major move.

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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 post The US$46,300 question: How low can Bitcoin go before buyers return appeared first on e27.

Malaysia is emerging as one of Southeast Asia’s clearest winners from the artificial intelligence infrastructure boom, but the benefits are not spreading evenly across its semiconductor industry, according to HSBC Global Investment Research.

In a report released last week, the research house said Malaysia, alongside Singapore and Vietnam, stands out as a primary regional beneficiary of the AI-driven technology upcycle. The evidence is visible in the country’s surging chip exports and its deepening electronics trade flows with the United States, mainland China, Taiwan and neighbouring Singapore.

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

Those flows matter because Malaysia sits at a critical point in the global chip supply chain. Fabricated chips are often shipped into the country for assembly, testing and packaging, or ATP, before moving on to device makers, cloud infrastructure providers and end customers. This back-end role has made Malaysia indispensable to global semiconductor production, even if the highest-margin parts of the industry still sit elsewhere.

But HSBC’s central point is more nuanced than a simple “AI boom lifts all boats” story. Malaysia is gaining from the global race to build data centres, train large AI models and secure computing capacity, yet the upside is concentrated among companies with direct exposure to AI-related demand.

A boom with uneven rewards

Firms linked to AI infrastructure, including selected chipmakers, advanced packaging providers and data centre operators, are seeing stronger demand as hyperscalers and technology companies continue to spend heavily on computing power.

The picture is less straightforward for companies tied to traditional consumer electronics. For them, the same AI boom can become a cost problem. Strong demand for memory chips, for instance, can push up input prices and squeeze manufacturers that rely on those components but do not directly benefit from AI-related orders. In some cases, higher memory costs could even slow production.

This split is important for Southeast Asia. The region’s electronics sector is often discussed as a broad beneficiary of supply-chain diversification and AI demand. In practice, exposure varies widely by product, customer base and position in the value chain. Malaysia’s semiconductor sector is large, but not every participant is equally plugged into the most profitable parts of the AI cycle.

HSBC said broader operating conditions remain resilient. The global electronics Purchasing Managers’ Index eased to 55.3 in July from 55.7 in June, while the Asia electronics PMI slipped to 54.7 from 55. Both remain firmly above the 50 mark that separates expansion from contraction, and are still ahead of their 12-month averages of 52.5 and 53.6, respectively.

That suggests the cycle remains healthy, even as pressure builds in specific parts of the supply chain. HSBC noted that input and output prices eased slightly in July and supplier delivery times improved marginally, but price pressures remain elevated and order backlogs are still growing.

The helium risk

A second concern is supply risk, particularly around key inputs used in chipmaking. HSBC highlighted lingering uncertainty linked to the Middle East conflict and its potential effect on critical materials such as helium.

Helium is used in parts of semiconductor manufacturing, especially in front-end wafer fabrication, where chips are created on silicon wafers. Any disruption to supply can therefore create complications for countries trying to expand fabrication capacity.

Malaysia is partly insulated because its largest semiconductor strength remains ATP, which depends less on helium-intensive processes and more on nitrogen. The country also has substantial domestic nitrogen production. Still, the risk is not irrelevant.

“Fortunately for Malaysia, ATP relies less on helium-intensive processes and more on nitrogen, for which the country has substantial domestic production,” HSBC said. “However, Malaysia’s wafer fabs do rely on helium, which means supply management still matters.”

Also Read: Malaysia’s GreatAsic raises US$6.9m to pivot nation from chip assembly to indigenous design

That distinction captures Malaysia’s current position well. Its dominance in back-end activities gives it resilience, but its ambition to move into more advanced front-end manufacturing exposes it to a different set of operational and geopolitical risks.

Moving up the stack

Malaysia’s long-term challenge is not simply to attract more semiconductor investment, but to capture more sophisticated parts of the industry. The government’s National Semiconductor Strategy, announced in 2024, commits US$6.12 billion and targets the training of 60,000 highly skilled local semiconductor engineers by 2030.

The strategy reflects a broader regional ambition. Southeast Asian economies want to move beyond being assembly bases and become deeper technology hubs. For Malaysia, that means expanding advanced packaging, building more front-end manufacturing capability and nurturing chip design talent.

The difficulty is that foundries are among the most capital-intensive industrial assets in the world. They require vast financing, reliable energy and water supplies, specialised infrastructure, and long-term customer commitments. These hurdles can be addressed over time with incentives and execution, but talent is harder to manufacture quickly.

HSBC flagged engineer retention as a key weakness. Average engineering wages in Malaysia’s manufacturing sector trail those in several Asian competitors, creating a clear risk of talent outflows, particularly to neighbouring Singapore, where pay is significantly higher.

Matching wages with wealthier economies will be difficult. But Malaysia can narrow the gap through targeted grants, tax incentives and schemes that improve the overall value proposition for skilled roles. It can also use targeted immigration policies to ease the talent constraint.

For founders, investors and operators in the region, this is the less glamorous but more decisive part of the semiconductor story. Capital announcements make headlines, but execution depends on whether countries can build and keep enough engineers, technicians and managers to run complex industrial ecosystems.

Malaysia’s geopolitical opening

One advantage Malaysia does have is geopolitical positioning. HSBC said the country’s ability to maintain constructive ties with both the US and China, while complementing Singapore’s more constrained land and resource base, could help it attract diversified foreign investment.

This matters as multinational chipmakers reassess location risk. Taiwan remains central to global semiconductors, South Korea is a memory powerhouse, and mainland China continues to invest heavily in self-sufficiency. But geopolitical tensions around major Asian chip hubs are forcing companies to think harder about redundancy and resilience.

Malaysia’s neutral reputation could therefore become a more valuable asset. Between January 2024 and March 2026, its semiconductor sector secured about US$22.52 billion in approved investments, including roughly US$20.31 billion in foreign direct investment.

The country’s roots in semiconductors go back more than five decades. Intel opened a chip assembly plant in Penang in 1972, shortly after Singapore entered the industry. Since then, Malaysia has grown into a major back-end semiconductor hub, accounting for 13 per cent of the global ATP market.

It is also, together with Singapore, one of only two ASEAN economies with front-end fabrication capabilities. Malaysia has particular strength in automotive power semiconductors, supported by German chipmaker Infineon’s manufacturing footprint in the country.

Also Read: Malaysia’s chip suppliers face rising pressure to prove cyber resilience

The scale is already substantial. Malaysia exported nearly US$110 billion of semiconductors in 2025, equal to around 23 per cent of gross domestic product.

The AI boom gives Malaysia a powerful tailwind. But HSBC’s report suggests the next phase will be harder than riding export momentum. The country must manage supply risks, avoid a two-speed industry, and solve the talent problem if it wants to move from being a crucial assembly hub to a higher-value semiconductor power.

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Southeast Asia’s financial services dealmaking held its ground in the first half of 2026, even as the total value of transactions dropped sharply, suggesting that buyers remain active but more selective in a market still shaped by high uncertainty.

According to EY’s latest financial services M&A analysis, the region recorded 31 publicly disclosed mergers and acquisitions in the first six months of the year, unchanged from the same period in 2025. But disclosed deal value fell to US$936 million from US$1.6 billion a year earlier.

Also Read: ‘M&A process in SEA is stuck in the dark age’: say match.asia co-founders

The headline number points to a market that has not frozen, but has become more cautious. Rather than the large, transformative deals that dominated parts of the previous cycle, Southeast Asia’s financial services activity in early 2026 was led by smaller and mid-sized transactions. For banks, insurers, fintech investors and asset managers, the focus appears to be on assets that can fill specific strategic gaps rather than aggressive expansion at any price.

“The stability in deal activity across Southeast Asia’s financial services sector reflected a market that remains engaged despite economic and geopolitical volatility,” said Stuart Last, EY-Parthenon Partner, Financial Services, Ernst & Young Solutions LLP.

He added that the fall in disclosed deal value suggests investors are pursuing opportunities with a clear strategic rationale, rather than chasing scale for its own sake.

Banks slow, wealth platforms gain ground

The split across sub-sectors shows how investor attention is shifting within the region’s financial services market.

Banking and capital markets remained the largest contributor by value, but activity declined. Deal volume in the segment fell to 14 from 20 a year earlier, while disclosed value dropped to US$669 million from US$1.1 billion.

That decline is not surprising. Banking deals in Southeast Asia are often shaped by regulation, ownership limits and the complexity of integrating legacy systems. While the region’s banks are still under pressure to digitise, improve cost efficiency and compete with fintech players, full-scale acquisitions can be difficult to execute, especially when interest rates, credit risk and capital requirements remain in focus.

Insurance moved in the opposite direction by deal count. The sector recorded nine deals in the first half of 2026, up from eight a year earlier. But disclosed deal value fell to US$123 million from US$478 million, indicating that activity was concentrated in smaller assets.

The more striking change came from wealth and asset management. Deal volume rose to eight from three, while disclosed value jumped to US$145 million from just US$800,000 in the first half of 2025.

That rise reflects one of Southeast Asia’s most persistent financial services themes: the growth of the affluent and mass-affluent population. As income levels rise in markets such as Singapore, Indonesia, Vietnam, Malaysia and Thailand, more consumers are seeking investment products, retirement planning tools and advisory services beyond traditional savings accounts.

For acquirers, wealth platforms can offer access to sticky customer relationships, fee-based revenue and digital distribution channels. In a region where financial literacy and investment participation are still uneven, firms that can combine trust, technology and local market access are becoming more attractive targets.

Last said the sharp rise in wealth and asset management deal value points to growing investor interest in platforms and capabilities that can capture demand from the region’s expanding affluent population.

Foreign buyers remain interested in Southeast Asia

EY’s data also suggests that international appetite for Southeast Asian financial services assets has not disappeared.

The number of non-Southeast Asian firms acquiring targets in the region fell to five in the first half of 2026 from seven a year earlier. However, the total disclosed value of these deals rose to US$410 million from US$344 million.

Also Read: How M&A can supercharge your startup’s success

That means fewer foreign acquirers were active, but those that did move were willing to commit larger sums. This matters because Southeast Asia continues to be viewed as a long-term growth market despite near-term volatility. The region has a young population, rising digital adoption, a large underbanked base in several markets, and increasing demand for credit, insurance and wealth products.

At the same time, operating across Southeast Asia is rarely straightforward. The region is not a single market. Financial services firms must navigate different regulators, licensing regimes, consumer behaviours, languages and levels of digital infrastructure. That complexity can slow dealmaking, but it can also make established local platforms more valuable.

EY expects larger transactions to return in the second half of 2026 if financing conditions improve and more scaled assets become available.

Global deal count rises, but megadeals thin out

The Southeast Asian pattern mirrors a broader global trend: more deals, but less value.

Globally, banks, insurers and asset managers publicly disclosed 1,137 financial services deals in the first half of 2026, up 3 per cent from 1,101 a year earlier. Yet total disclosed deal value fell to US$134.5 billion from US$191.3 billion.

The drop was largely driven by a thinner pipeline of megadeals. EY recorded 25 transactions above US$1 billion in the first half of 2026, representing 80 per cent of total deal value. That compares with 37 such deals in the first half of 2025 and 55 in the second half of 2025.

The concentration of value among the largest transactions remained high. The ten biggest global financial services deals accounted for US$78.7 billion, or 58 per cent of total value. The top 20 deals accounted for US$100.5 billion, or 75 per cent.

Omar Ali, EY Global Financial Services Leader, said financial services firms have adapted to heightened uncertainty as part of normal operating conditions. But he noted that unpredictability, slower global growth, inflation and supply shocks continue to affect deal value.

“Despite the number of transactions rising, deal value in the first half this year across the world’s major markets is down on 2025 levels, as significantly fewer transactions completed over the US$1 billion mark,” he said.

Asia and Oceania weaken, but cross-border interest grows

Across Asian and Oceanian markets, the first half of 2026 was softer than in Southeast Asia. Publicly disclosed financial services M&A fell 14 per cent to 147 deals from 170 a year earlier. Total disclosed value slipped to US$15.8 billion from US$17.8 billion.

Also Read: M&A in Asia: A strategic roadmap for venture builders

Banking and capital markets deal volume in the broader region declined to 77 from 87, though deal value rose to US$11.3 billion from US$6.4 billion. Insurance weakened more clearly, with volume falling to 31 from 41 and value dropping to US$2.1 billion from US$5 billion. Wealth and asset management also declined, with volume falling to 39 from 42 and value sliding to US$2.4 billion from US$6.5 billion.

However, foreign interest in Asian and Oceanian targets increased. Non-regional acquirers completed or announced 28 deals, up from 23, while disclosed value rose to US$1.9 billion from US$1.6 billion.

For Southeast Asia, the message is mixed but not gloomy. Dealmakers are not retreating from the region. They are becoming more disciplined, more sector-specific and more careful about valuation. The next phase of activity may depend less on whether buyers have appetite, and more on whether sellers are willing to meet the market.

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For years, Southeast Asia’s digital economy has grown on top of technologies built elsewhere. Cloud infrastructure, operating systems, search, social media, e-commerce tools and, more recently, large language models have largely come from the US and China. The region adapted quickly, but rarely controlled the deepest layers of the stack.

Artificial intelligence is forcing governments and founders to revisit that bargain.

Also Read: The unexpected ways AI is already changing Malaysia’s economy

Malaysia’s National AI Action Plan 2026-2030, also referred to as AI Nation 2030, places this question at the centre of its digital strategy: can the country move from being a buyer of AI tools to a producer of AI products, infrastructure and talent? The plan’s answer is built around sovereign AI — the ability to build, train and deploy AI using domestic infrastructure, data and workforce capabilities.

This is not just a policy slogan. For startups, it could become a practical competitive advantage. In a region as linguistically and culturally complex as Southeast Asia, models trained primarily on foreign datasets often miss local nuance. That gap matters in areas such as education, public services, healthcare, agriculture, financial inclusion and legal compliance, where context is not a nice-to-have but the product itself.

Local context as a moat

The strongest argument for sovereign AI is not that every country needs to rebuild OpenAI, Google or Anthropic from scratch. It is that global models, however powerful, are not always designed for local realities.

Malaysia’s plan recognises this through its Secure and Localised AI Ecosystem initiative, which calls for trusted local datasets and indigenous AI models. One priority is the development of large language models fluent in Bahasa Melayu and aligned with Malaysian cultural norms, including through collaboration with institutions such as Dewan Bahasa dan Pustaka.

For founders, this creates room to build where global platforms are weakest. A generic chatbot may perform adequately in English-language customer support, but it may stumble when handling Bahasa Melayu, Manglish, code-switching, dialects, religious sensitivities or public-sector terminology. In classrooms, government offices or rural advisory services, those mistakes can damage trust.

The same logic applies across Southeast Asia. Indonesia, Thailand, Vietnam and the Philippines all face similar challenges: large populations, diverse languages, uneven digital literacy and public services that need to work beyond metropolitan centres. A model that understands a population’s language, regulations and social context can outperform a larger but less grounded system in specific high-value use cases.

This is where local startups may find their wedge. They do not need to win the global foundation-model race. They can build domain-specific AI tools that combine global advances with local data, workflows and compliance requirements.

From AI adoption to AI production

Malaysia’s ambition is also economic. AI Nation 2030 aims to place the country among the top 10 in global AI indices, add an incremental 1.2 percentage points to GDP growth and create 300,000 new jobs by 2030.

Those are aggressive targets, but they reflect a broader shift in Southeast Asia’s thinking. The region’s internet economy has grown rapidly — Google, Temasek and Bain estimated it at US$263 billion in gross merchandise value in 2024 — yet much of the value still accrues to platform owners, cloud providers and chipmakers outside the region.

Also Read: How can Malaysia leverage AI for growth and not see it as a threat?

Malaysia wants a larger share of the value chain. Its plan uses public-private partnership “Impact Engines” to push local firms into higher-value AI segments, rather than leaving them as downstream users of foreign tools. One example is the AI Hub for the Manufacturing Ecosystem, which builds on Malaysia’s existing strength in semiconductors and electronics.

This is a sensible starting point. Malaysia is already part of the global chip supply chain, particularly in assembly, testing and packaging. Applying AI to improve yields, predict maintenance issues, optimise energy use and manage supply chains could help local manufacturers move up the ladder. It may also make the country more attractive to high-value foreign direct investment at a time when companies are diversifying supply chains across Asia.

For startups, the opportunity lies in the middle layer: AI applications for factories, logistics providers, farms, banks, schools and government agencies that need solutions adapted to local operations. These are not always glamorous markets, but they are often where durable revenue is built.

Data and compute decide who gets to build

Sovereign AI ultimately depends on two scarce inputs: data and compute.

Malaysia’s plan proposes an AI-ready data ecosystem that aggregates priority datasets across sectors and turns them into trusted, purpose-driven data products. A National Data Exchange would allow startups and institutions to discover and license datasets under clearer terms.

If executed well, this could address one of the biggest constraints for AI startups in the region. Many founders can access open-source models, but not the high-quality local datasets needed to make those models useful. Data is often fragmented across ministries, state agencies, corporates and legacy systems. Access can be slow, opaque or legally uncertain.

The compute side is just as important. Training and fine-tuning AI models requires expensive hardware, and reliance on foreign cloud providers can become a strategic vulnerability for governments handling sensitive data. Malaysia’s proposed National Supercomputing Centre and AI Sukuk financing mechanism are designed to expand access to high-performance compute for local innovators.

The key question will be implementation. If data access remains bureaucratic or compute is captured by large incumbents, startups will see little benefit. But if the infrastructure is affordable and fairly governed, it could reduce the entry barrier for Malaysian AI companies and research teams.

Trust as a market advantage

AI adoption will also depend on public confidence. Malaysia’s plan includes a governance framework aligned with the National Guidelines on AI Governance and Ethics, alongside an AI Trust Function to monitor and respond to risks. It also proposes a National AI Classification mechanism for “Made by Malaysia” AI products.

For startups, this may sound like more compliance work. In practice, clear rules can help. Regulated sectors such as finance, healthcare, education and public services will not adopt AI at scale without confidence that systems are safe, explainable and accountable.

A national classification framework could become a trust signal for buyers, particularly if Malaysia wants its AI products to travel across ASEAN. The region is still early in building interoperable AI governance. A credible Malaysian certification could help local companies sell into neighbouring markets that face similar concerns but may not have the same institutional capacity.

Inclusion will determine the outcome

Malaysia’s sovereign AI strategy is not only about building elite technology. It also targets micro, small and medium enterprises, which make up 84.4 per cent of businesses in the services sector. These firms often lack specialist talent and cannot afford bespoke AI systems. The AI for MSMEs initiative proposes modular, pre-vetted tools embedded into platforms they already use.

Agriculture is another test case. A scalable agristack using local data could support precision farming, weather prediction and crop advisory services. For a country seeking stronger food resilience, AI will only matter if it reaches farmers, cooperatives and small suppliers, not just large agribusinesses.

This inclusive angle is important for Southeast Asia. If AI primarily benefits large companies in capital cities, it may deepen existing divides. If it improves productivity for small businesses, schools, clinics and farms, it could become a broader development tool.

Also Read: AI demand lifts Malaysia’s chip sector, but not every player wins

The final piece is talent. Malaysia’s plan includes a Global Talent Network, Digital e-Residency Pass, AI Talent Concierge and large-scale reskilling efforts. These measures acknowledge a hard truth: sovereign AI cannot be built with infrastructure alone. The country needs engineers, product managers, policy specialists, domain experts and teachers who understand both technology and local problems.

Malaysia’s sovereign AI push is ambitious, and many parts remain dependent on execution. But its underlying bet is sound: in AI, local context is not a weakness. It may be the clearest moat Southeast Asian startups have.

The post Malaysia’s sovereign AI bet: Local context becomes the next startup moat appeared first on e27.

Volume went up. Quality didn’t. The bottleneck moved somewhere nobody is looking.

Every marketing team I speak to in Southeast Asia has the same story about generative AI: we make ten times more creative than we did eighteen months ago, and we’re not convinced any of it is better. That instinct is now measurable. New research from WARC with TikTok and LIONS Advisory, reported by PPC Land, surveyed 400 marketers and found that while 88 per cent are producing more creative since adopting AI, only 45 per cent see a meaningful lift in quality.

The interesting part isn’t the gap. It’s the reason for it. Two-thirds of those marketers said they brief generative models primarily with demographic data. Nearly six in ten of the same group said demographic segmentation no longer works. They are feeding the machine inputs they have already told researchers are broken. Only 17 per cent consistently brief with anything richer — community context, behavioural signal, actual audience tension.

The report’s framing is the sharpest line in it, and it deserves repeating in plain terms: this is not a technology gap. It is an intelligence gap.

The evaluation layer nobody costed

Here is what changed, and why it is easy to miss.

A brief has never really been a document. It has been the start of a conversation. It went to a strategist, who pushed back on the segment. It went to a creative director, who asked what the person actually feels at the moment of purchase. It went to an art director, who threw out the first three routes. By the time work reached a client, a thin brief had been quietly repaired four or five times by people whose job was, in part, to notice that it was thin.

Those stages were slow, and slowness was the point. They were also expensive, and so they were the first thing compression removed. When production timelines collapse from three weeks to three days, the repair layer goes with them. The brief no longer passes through a series of sceptical humans. It passes into a model, which is constitutionally incapable of scepticism about its own inputs and will produce forty confident variants of a bad idea as readily as forty of a good one.

So the weakness that used to be absorbed by the process now lands directly in the output, at volume, and with the polish of professional work. That is a much worse failure mode than the one it replaced. A bad brief used to produce visibly bad work that somebody caught. Now it produces plausible work that nobody catches, because it looks fine.

Also Read: Moving past the chatbox: The hidden risks of agentic AI and MCP in enterprise infrastructure

Demographics survive because they are already in the template

Why do teams keep briefing with data they don’t believe in? Not conviction — inertia.

Age brackets and income bands are already sitting in the planning deck. They are already in the media plan, the audience field, the campaign naming convention. They require no new work, no new tooling, no argument with anyone. Behavioural and community insight requires all four. Under deadline, the default wins every time, and the default is a demographic.

GWI made this point publicly last week in a rather good line — that age brackets are the laziest segment in marketing, and the differences inside a generation are larger than the differences between generations. They are right, and it is worth noticing how rare that argument is. Look across a week of published content from the marketing-technology category and a clean division appears. The audience research firms publish findings that stop at the statistic. The workflow and listening platforms publish features that start at the publish button. Almost nobody addresses the space between the two, which is precisely where the intelligence gap lives.

Why Southeast Asia feels this first

Because the compression here is more severe. A regional team in Singapore is routinely running six to eleven markets, in several languages, against budgets that would cover two markets in Europe. Local nuance is not a refinement; it is the entire job. And it is exactly the layer that demographic briefing flattens.

Feed a model “women 25–34, urban, middle income” and it will return something that could run in Jakarta, Manila or Kuala Lumpur and land properly in none of them. The output will be grammatical, on-brand and completely generic. Multiply that by eleven markets and a weekly cadence and you have built a very efficient machine for producing content nobody remembers.

Also Read: No fans, no fridges, just paint: ZERC’s founder on cracking SEA’s cooling crisis

Rebuilding the layer, cheaply

The teams pulling ahead have made one structural change: they treat the brief as the product. Not the deck, not the asset — the brief. Whoever controls the quality of the input now controls the quality of everything downstream.

That change is showing up in the tooling at both ends of the pipeline. At the front, platforms that decode live category data into audience tensions and evidence-backed briefs, so the input carries something observed rather than something assumed. At the back, a newer class of businesses built purely around execution — Touchigh, for instance, which helps Chinese cross-border sellers reach American buyers by automating both AI-search visibility and native English social content, and scores that content for predicted performance before it publishes rather than reporting on it after.

That last detail matters more than the category it sits in. A young execution company, serving SMEs at a few hundred dollars a month, has independently arrived at the same conclusion: the judgement call has to happen before the asset ships, or it doesn’t happen at all. Front end and back end are solving different halves of one problem — stopping weak inputs entering the system, and stopping good ones degrading on the way out.

What the operating numbers suggest is that the repair layer can, in fact, be rebuilt cheaply enough to survive a deadline. Pitching a UK hospitality group against considerably larger shops, the agency SAMY documented category planning collapsing from weeks to roughly thirteen minutes per brief, with predicted click-through accuracy running about three times better than human estimation. They won the retainer.

Read that carefully, because the speed is not really the story. The story is that the judgement nobody could afford is now something you can afford on every single brief — and a judgement that only happens when there’s time for it isn’t a standard, it’s a luxury.

The question worth sitting with

The uncomfortable version of the WARC finding is that most teams already know their inputs are wrong and ship on them anyway, because fixing the input costs more this week than shipping the output does.

That maths is changing. When evidence-grade audience intelligence takes minutes rather than weeks, the excuse for briefing on a demographic quietly disappears — and so does the defence when the work underperforms.

So: of the briefs your team wrote this quarter, how many could you trace back to something you actually observed about the audience, and how many were the template with a new date on top?

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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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The next phase of digital payments may not be defined by faster checkouts or cheaper transfers, but by a more uncomfortable question: who or what is being trusted to move money?

As businesses begin experimenting with AI agents that can search for suppliers, compare prices, negotiate terms, initiate payments, and reconcile invoices, the old assumptions around financial control start to fray. A human no longer clicks every button. A finance team may not manually approve every step. In some cases, software will act on behalf of a company, within rules set in advance.

Also Read: The next AI payments boom may happen in the back office

That shift sits at the centre of “Beyond Automation: Defining Agentic Global Payments”, a report by Sunrate and Mastercard. Its central argument is straightforward: automation alone is not enough. If AI agents are to handle commercial decisions involving millions of US dollars, companies need more than speed. They need accountability.

The report calls this missing infrastructure the “Trust Layer”, a framework that allows businesses to verify an agent’s identity, understand its authority, and trace what it has done. In other words, the future of payments will not just depend on whether AI can act intelligently. It will depend on whether organisations can prove that these actions were authorised, limited, and auditable.

From Know Your Customer to Know Your Agent

For the past decade, much of fintech has been shaped by Know Your Customer (KYC) rules. Banks, payment companies, and fintech startups have built systems to verify that users are who they say they are, screen them for risk, and monitor suspicious activity.

Agentic commerce introduces a new layer of complexity. If an AI agent places an order, books travel, pays a supplier, or moves funds across borders, the payment ecosystem needs to know more than the identity of the company behind it. It must also understand the identity and authority of the agent itself.

This is where “Know Your Agent”, or KYA, comes in.

KYA is not simply a branding exercise. It points to a practical set of controls: verifying an AI agent, defining what it is allowed to do, recording the intent behind a transaction, and ensuring that actions remain within commercial and policy boundaries. An agent authorised to buy office supplies, for example, should not be able to approve a large foreign exchange transfer. A procurement agent with a US$10,000 spending limit should not be able to split payments to bypass that limit.

Also Read: From chatbots to payment agents: AI’s next role in SEA commerce

For Southeast Asia, where many companies already operate across fragmented markets, currencies, payment methods, and compliance regimes, this matters. A regional startup may have suppliers in Vietnam, customers in Indonesia, finance operations in Singapore, and banking relationships across several jurisdictions. Adding autonomous agents into that mix without governance could create a risk environment that is difficult to monitor.

The three pillars of the Trust Layer

The report breaks the Trust Layer into three broad pillars.

The first is credential protection. In today’s payment systems, tokenisation is already used to replace sensitive card or account details with secure digital tokens. In an agentic payments environment, this becomes even more important. AI agents should not be passing around raw card numbers, bank credentials, or account information. If those agents are compromised, the damage could be significant.

The second pillar is intent capture. This means securely transmitting the user’s budget, preferences, constraints, and instructions along with the transaction. In human terms, it is the difference between saying “buy the cheapest ticket” and “buy a refundable economy ticket under US$700, departing after 7pm, with no overnight layover”. For businesses, intent capture allows systems to determine whether an agent acted in line with approved instructions.

The third pillar is KYA and governance. This is the architecture that verifies the agent’s identity and sets strict permission boundaries. It includes authentication, policy enforcement, audit trails, and the ability to revoke or modify permissions when needed.

These controls may sound technical, but their commercial importance is simple. Businesses cannot delegate financial decisions to agents if they cannot later explain what happened, why it happened, and whether it was allowed.

Why many AI projects do not make it past pilots

The urgency is not theoretical. According to Gartner, at least 50 per cent of AI projects were abandoned last year after the proof-of-concept stage. The reasons included poor data readiness, high costs, and a lack of risk control.

That last point is particularly relevant for payments. In many companies, AI pilots are still treated as productivity experiments. Teams test whether a model can draft emails, summarise documents, or automate customer support. Payments are different. A bad recommendation may waste time. A bad transaction may move real money, breach regulations, or damage a company’s relationship with banks and suppliers.

Also Read: The scarcity mindset is killing creativity, not AI

For Southeast Asian startups, this creates both a warning and an opening. The warning is that building a clever agent is not enough. A product that can automate procurement or treasury workflows may impress in a demo, but enterprise customers will ask harder questions before deploying it in live payment flows.

Who approved this transaction? What data did the agent use? Can the company prove that the payment matched its internal policy? Can a bank or payment service provider trace the chain of authorisation? What happens if the agent is tricked by fraudulent instructions?

The opportunity lies in answering those questions better than competitors. The strongest companies in this space may not be the ones with the most sophisticated AI interface, but those that combine automation with controls that banks, CFOs, auditors, and regulators can trust.

Why the ecosystem matters

A Trust Layer cannot be built by a single startup in isolation. Agentic payments will require coordination across banks, card networks, payment service providers, enterprise software platforms, and regulators.

This is where established networks such as Mastercard are likely to play a significant role. Card networks already sit across large parts of the payment ecosystem and have experience with tokenisation, identity standards, fraud management, and dispute processes. Extending governed, traceable tokenisation into autonomous payment flows is a logical next step.

Payment service providers and cross-border platforms also matter, particularly in Southeast Asia. The region’s businesses often deal with multi-currency payments, varied settlement timelines, and uneven levels of banking infrastructure. If AI agents are to operate across borders, they will need infrastructure that can translate business intent into compliant payment execution across different markets.

Regulators will also have to catch up. Many existing rules assume a human actor at key decision points. Agentic systems challenge that assumption. Over time, authorities may need clearer standards on agent identity, liability, consent, auditability, and operational resilience.

Trust as a competitive advantage

The rise of AI agents in payments is often framed as a story about efficiency. There is truth in that. Agents could reduce manual work, speed up reconciliation, and help businesses optimise costs across suppliers and currencies.

But efficiency will not be the deciding factor if companies fear losing control.

Also Read: The next AI payments boom may happen in the back office

The more important race is to build systems where autonomy does not mean opacity. Businesses will need to know not only that an agent completed a task, but that it did so within defined limits. Banks will need confidence that transactions are legitimate. Payment networks will need ways to trace credentials and intent. Regulators will need evidence that responsibility has not disappeared into a black box.

For Southeast Asia’s startup ecosystem, the message is clear. The next wave of payments innovation will not be won by speed alone. It will be won by companies that can make AI agents accountable.

The future belongs not just to agents that are smart enough to act, but to systems that are safe enough to trust.

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Ten years into building, the annual planning cycle stops being useful. A year is too short a unit to learn anything from. It’s long enough to feel like progress and short enough to hide the fact that the money isn’t made evenly across time. It shows up in bursts, during the stretches when conditions favour you, and it gets defended during the stretches when they don’t.

Most operators know this and rarely say it plainly, because it sounds like admitting the good years were luck. They weren’t luck. They were timing, and timing is a decision made years before the moment it pays off.

You can’t cause a cycle, but you can force your position in it

Andrew Carnegie’s steel business went through two price collapses, in the 1870s and again in the 1890s. Competitors did the obvious thing both times: cut production, lay off crews, wait for demand to return. Carnegie ran his mills at a loss instead, because construction costs were cheap and rivals were selling assets at fire-sale prices to survive. His instruction to a subordinate was “small profits and large sales” while everyone else retrenched. He wasn’t predicting the recovery. He was buying it in advance, at a discount, while the rest of the industry was too scared to spend.

Toyota’s position going into 1973 is the cleaner example of catching a shift rather than buying one. American demand ran on large-displacement engines, and Japanese compacts held a rounding error of US market share. The 1973 oil embargo changed the math on fuel cost overnight, and Japanese import share in the US moved from roughly 9 percent in 1976 to 21 percent by 1980. Toyota didn’t cause the oil shock. It had spent the prior decade building a fuel-efficient car for a market that didn’t want one yet, so when the market changed, the product was already on the lot.

Also Read: 15 Thai AI companies betting on products, not hype

Neither company controlled the macro event. Both controlled whether they were structurally ready the moment it hit, in cash, in capacity, in product that already existed rather than product still waiting to be built. That’s the actual answer to whether a tailwind can be forced: the conditions can’t be forced, but the position relative to them can.

Built to survive the gap between tailwinds

Corning is the case for doing this more than once. It’s a 170-year-old glass manufacturer that ran on Pyrex and CorningWare through most of the twentieth century, invented low-loss optical fibre in 1970 decades before the internet needed it, rode the fiber boom of the late 1990s, absorbed the 2001 fibre bust without gutting its glass science team, sold off Pyrex in 1998 to focus entirely on advanced glass, and turned a shelved forty-year-old formula into Gorilla Glass in 2007 after a call from Steve Jobs. Corning didn’t get one cycle right. It built a company that could survive the years between cycles, so it was still standing when the next one arrived.

Samsung’s memory chip business runs the same logic on a shorter clock. In 2008, when the financial crisis hit and every other DRAM maker cut capital spending to preserve cash, Samsung increased it, and repeated the move in the 2012 and 2019 downturns. Competitors treated the downturns as something to survive. Samsung treated them as the one window where capacity was cheap and competitors were retreating, which is a structurally different decision, and it’s a large part of why Samsung still leads the category.

Also Read: Your product is not your startup

None of these four were guessing about macro timing. Each decided, years ahead of the shift, what kind of company it wanted to be caught being when conditions turned, then built the balance sheet, product, or manufacturing base to match before the turn happened.

Conclusion

The planning horizon matters more than the plan itself. A company that budgets in single years will always be reacting to the season it’s already in. A company that plans in multi-year cycles can spend the quiet years on the unglamorous work: building capacity, buying distressed assets, shipping a product nobody’s asking for yet, so it isn’t scrambling to catch up when conditions turn favourable.

The harder question isn’t whether the next favorable window is coming. It always is. It’s whether the quiet years get spent building something ready to catch it, or just something that survived long enough to still be there when it shows up.

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Singapore’s biotech sector has long had the ingredients of a serious life sciences hub: strong universities, public research funding, hospital networks, and a government keen to pull high-value industries into the city-state. What it has had less of is a steady stream of venture-backed drug developers built to compete internationally from day one.

K2 Therapeutics is trying to fit that gap.

The Singapore-based biotechnology company has raised US$50 million in seed financing from MPM BioImpact, the US investment firm that founded the company in 2024. The round has already helped K2 expand its pipeline to eight therapeutic programmes across multiple modalities, including antibody-drug conjugates and T-cell engagers, with assets ranging from pre-clinical candidates to clinical-stage programmes.

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

That is a wide starting point for a newly formed biotech company. In drug development, “pre-clinical” typically means a therapy is still being tested in the lab or in animal studies, while “clinical” means it has entered human trials. The jump between those stages is where many young biotechs struggle, as costs rise sharply and scientific promise meets regulatory, safety and manufacturing realities.

K2’s model is to identify drug candidates internationally and advance them through in-house development. In practice, that means the company is not beginning solely as a discovery lab. It is looking globally for promising assets, then using capital and development expertise to move them through the difficult middle stretch of biotech: from candidate selection to human proof-of-concept, and potentially towards commercial partnerships or approvals.

A platform built around global asset sourcing

K2 Therapeutics said it expects to grow its portfolio further through asset acquisition, capital deployment and development. The approach reflects a broader shift in biotech financing, where investors increasingly back teams that can source overlooked or underdeveloped science globally, rather than relying on a single internal platform.

This matters in Southeast Asia because the region’s biotech ecosystem is still young compared with those in the US, Europe, China, South Korea and Japan. Singapore has strong research capabilities and hosts major pharmaceutical manufacturing and regional headquarters operations, but building venture-scale therapeutic companies remains difficult. Drug development takes years, requires specialised talent, and depends on access to sophisticated clinical, regulatory and manufacturing infrastructure.

A US$50 million seed round gives K2 unusually deep early backing by regional standards. Seed rounds in software can be used to build a product and test the market. In biotech, that money is often spent on experiments, toxicology studies, manufacturing preparation, regulatory filings and early clinical work before any revenue is in sight. The scale of K2’s financing signals that MPM BioImpact is not treating the company as a small exploratory bet, but as a vehicle to assemble and advance a serious therapeutic pipeline.

MPM BioImpact manages more than US$3.5 billion in assets and has a long history of forming and financing biotechnology companies. Its decision to found K2 in Singapore is also notable at a time when global life sciences investors are looking beyond the traditional Boston-San Francisco axis for scientific talent, clinical access and new deal flow.

A CEO with commercial experience

Alongside the financing, K2 has appointed Ying Huang as CEO. Huang was previously chief executive and a board member of Legend Biotech, where he oversaw the development and commercialisation of cell therapies and the company’s expansion to more than 3,000 employees. Before Legend, he was head of biotechnology equity research at Bank of America Merrill Lynch.

That mix of operating and capital markets experience is important for a company like K2. Biotech CEOs are not only expected to understand the science; they must also raise large amounts of capital, prioritise programmes, manage clinical risk, negotiate with pharmaceutical partners, and explain complex data to investors and regulators.

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

“By combining global asset sourcing with experienced development leadership,” K2 can rapidly advance differentiated therapeutic candidates with the potential to address significant unmet medical needs, Huang said.

The quote is measured, but it captures the thesis. K2 is betting that the bottleneck in biotech is not only invention. It is also execution: knowing which assets deserve capital, which should be stopped early, and how to move the strongest candidates through a highly regulated system.

Why ADCs and T-cell engagers are attracting attention

Among K2’s notable programmes are antibody-drug conjugates, or ADCs, and T-cell engagers. Both areas have drawn intense investor and pharmaceutical interest globally.

ADCs are often described as targeted cancer therapies. They combine an antibody, which seeks out specific markers on diseased cells, with a toxic payload designed to kill those cells more precisely than traditional chemotherapy. The field has seen several major acquisitions and licensing deals in recent years as drugmakers race to build oncology pipelines.

T-cell engagers work differently. They are designed to bring immune cells, particularly T cells, into close contact with cancer cells so the immune system can attack them. The idea is powerful, though developing safe and effective T-cell engager therapies can be scientifically and clinically challenging.

K2 has not disclosed the specific diseases targeted by its eight programmes, nor the terms of any asset acquisitions or licensing arrangements. That leaves key questions unanswered: how differentiated the candidates are, how much clinical data already exists, and how K2 will decide which assets deserve priority.

The competitive field

K2 Therapeutics will be entering a crowded global race. In Asia, companies such as China’s Akeso, Kelun-Biotech, DualityBio and RemeGen have drawn attention for antibody-based oncology drugs and ADC pipelines. Singapore has also produced antibody and oncology-focused biotechs such as Hummingbird Bioscience, while larger global players including Genmab, BioNTech, AstraZeneca, Gilead and Daiichi Sankyo are investing heavily in next-generation cancer therapies.

The competition is not only for patients or market share. It is also for assets, clinical trial sites, scientific talent, manufacturing capacity and partnership attention from big pharma. For a Singapore-based biotech, that means regional credibility alone will not be enough. K2 will need to show that its pipeline can stand up to global scientific scrutiny.

Still, Singapore offers some advantages. Its regulatory environment is considered predictable, its biomedical research base is deep for a country of its size, and its position in Southeast Asia gives companies a regional operating base close to diverse patient populations. For founders and investors, the challenge is turning those strengths into globally competitive drug development companies rather than regional outposts for multinational pharma.

K2’s US$50 million seed financing is therefore more than another funding announcement. It is a test of whether Singapore can host the next generation of biotech companies that are not merely doing research, but assembling, developing and potentially commercialising therapies for global markets.

Also Read: From lab to factory floor: ChemT nets US$4M to make cell therapies easier to manufacture

For now, K2 Therapeutics has capital, a sizeable early pipeline and a CEO who has taken advanced therapies from development into commercial scale. The harder part begins next: proving that the assets it has gathered can survive the long, expensive and unforgiving path from promising science to medicines that patients can actually use.

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One of the laziest ambitions in strategy is the desire to own the whole stack.

It sounds bold in leadership meetings. It sounds defensible in investor conversations. It sounds like control. If we own more of the value chain, more of the customer relationship, more of the workflow, more of the economics, then surely we are building a stronger position.

Often, we are doing the opposite.

In many markets, trying to own everything is not a sign of strength. It is a sign that the firm has not yet understood where leverage actually lives. Ownership expands surface area. It increases execution burden. It drags the company into activities where it may have no real advantage. It creates cost, complexity, and management sprawl. Worst of all, it can distract leaders from the far more important question. Which part of this system truly matters enough that others will keep orienting around us, even if we do not own the rest.

That is where strategic chokepoints come in.

The strongest positions often sit between assets, not on top of them

A surprising amount of strategic thinking still assumes power sits with the party that owns the most assets. More infrastructure, more products, more distribution, more channels, more touchpoints. The image is imperial. The larger footprint must mean the stronger position.

Real markets are often organised differently.

Some of the most durable positions sit not with the actor that owns everything, but with the actor that sits at the point where different things have to come together. The place where supply meets verification. The place where data becomes decision. The place where activity becomes auditable. The place where users become billable. The place where risk becomes governable. The place where systems that do not naturally speak to one another must suddenly agree.

A chokepoint is where uncertainty has to be resolved

The clearest way to identify a real chokepoint is to stop asking where activity happens and start asking where uncertainty must be settled before activity can continue.

That is the deeper strategic move.

In many markets, the most valuable position is not at the point of creation or consumption. It is at the point of resolution. The place where someone has to decide whether identity is real, whether payment can be trusted, whether compliance is sufficient, whether a model output is acceptable, whether a supplier is approved, whether risk is within tolerance, whether a transaction can be recorded as final, whether a failure can be recovered without chaos.

Also Read: Why Southeast Asian startups should stop treating Europe as one market

Those moments are strategically rich because they are not optional. The surrounding market can innovate, fragment, diversify, and compete aggressively, but when it reaches a point where uncertainty must be converted into confidence, somebody has to perform that function.

Whoever performs it well can become disproportionately powerful.

Leverage is usually designed at the point where others need certainty

The original strategic instinct behind many great businesses is not, how do we own more. It is, how do we become the answer at the moment others need certainty faster than they can create it themselves.

That is a much more intelligent design question.

A strategic chokepoint can emerge around trust. It can emerge around technical compatibility. It can emerge around data custody. It can emerge around regulatory interpretation. It can emerge around reconciliation, recovery, settlement, or proof. What matters is not the category name. What matters is whether others start depending on that point to turn ambiguity into action.

This is why the best chokepoints often feel smaller than the markets they influence. They are concentrated. They do not need to carry the whole weight of the system. They only need to sit at the moment where the system cannot proceed safely, credibly, or efficiently without them.

Once that happens, leverage follows almost naturally.

The weak version of this idea is bottlenecking, the strong version is coordination

Not every chokepoint is strategically healthy. Some are little more than bottlenecks. They create friction without adding enough legitimate value. They slow the system down, tax it, or trap participants through inconvenience rather than through necessity. Those positions may produce short term leverage, but they also invite resentment, workaround behaviour, regulation, or eventual displacement.

The stronger version of a chokepoint is different. It improves coordination.

A legitimate chokepoint does not merely obstruct passage. It makes passage safer, faster, more intelligible, more governable, or more trusted. It reduces transaction cost. It lowers institutional anxiety. It gives multiple participants a shared basis on which to act. It helps the market function at a level of scale or complexity that would otherwise be difficult to sustain.

That is why the best strategic chokepoints are not experienced as pure extraction. They are experienced as useful compression. They narrow the system at the exact place where narrowing is valuable.

This is also why they last. Participants may not enjoy dependence, but they will tolerate it when the alternative is disorder.

Designing a chokepoint means designing a habit in the market

A useful way to think about strategic leverage is that the company is not simply building a product or service. It is trying to build a habit in the market.

Not a consumer habit in the narrow behavioural sense, but a systemic habit. A repeated pattern in which others begin to assume that before they proceed, they should pass through this layer. Before a model is trusted, it must be reviewed here. Before a vendor is activated, it must be cleared here. Before value is counted, it must be recorded here. Before a workflow scales, it must connect here.

That habit is what turns a useful position into a durable one.

Also Read: The myth of the neutral stack: Why SEA startups can no longer sit on the fence

The deeper point is that leverage compounds when the market starts organising itself around your existence without having to be forced. Once institutions begin embedding you into policy, process, reporting, integration design, or internal governance, the relationship is no longer just commercial. It becomes operational and cognitive. You are no longer merely chosen. You are expected.

That is the point at which designing a chokepoint starts to look less like product expansion and more like market architecture.

The danger is becoming so powerful that you weaken your own legitimacy

The more important a chokepoint becomes, the greater the temptation to overuse it. Companies start increasing take rates, privileging their own offers unfairly, reducing transparency, or changing rules in ways that maximise extraction at the expense of trust. That is usually the beginning of strategic decay, even if the financial effects take time to show.

A chokepoint remains durable only while participants believe the power attached to it is being exercised in a way that preserves the health of the broader system. Once that belief breaks, market actors start building alternatives, regulators become more interested, and internal defenders inside customer organisations become less willing to protect the relationship.

This is why the strongest chokepoints are governed, not merely exploited.

They carry a burden of stewardship. The company at the centre has to act in ways that keep the market willing to route through it. That means predictability, fairness, quality control, and enough restraint that dependence does not start to feel intolerable.

In other words, the position has to remain useful enough to stay legitimate.

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