The lesson from Washington’s intervention against Anthropic’s Fable 5 and Mythos 5 is not that governments are powerless over AI. They are not. A state can order a company to switch off a model. It can gate access. It can ration release to approved organisations. It can turn a commercial launch into a political permissioning process overnight.

But that is not the same as containing the capability.

That distinction matters because AI capability no longer lives only inside one model, one company, or one release. It is increasingly a moving frontier produced by falling compute costs, model-learning curves, open-weight diffusion, and rival systems constantly catching up with whatever looked unique a few months earlier.

When the US government forced Anthropic to disable Fable 5 and Mythos 5, the objective was clear: keep a dangerous vulnerability-finding capability out of hostile hands. The problem was also clear. Anthropic itself indicated that the capability in question was already obtainable from other models, including OpenAI’s GPT-5.5. The order removed a product. It did not remove the underlying ability.

That is the mechanism the intervention exposed.

AI containment through model removal runs into two forces moving in the opposite direction.

The first is falling cost. The price of delivering a fixed level of machine intelligence has collapsed. A level of model quality that cost roughly US$20 per million tokens in late 2022 cost roughly seven cents two years later. That does not mean every frontier capability can be replicated instantly or cheaply. Training, chips, data centres, power, and talent still matter. But the direction of travel is unmistakable: the cost of delivering useful machine intelligence keeps falling, and each decline lowers the barrier for competitors to reproduce more of what the frontier once made scarce.

The second force is catch-up. Frontier AI is not a single static asset. It is a moving pack. Closed models lead, open models narrow the gap, foreign models improve, in-house systems absorb specific capabilities, and fine-tuned variants spread into specialised uses. The thing that looks like a unique model capability at launch is often a broadly reproducible capability months later, sometimes sooner. The frontier moves, but the field behind it moves too.

Also Read: How AI and blockchain could make commerce decisions more accountable

Those two mechanisms together make government model bans structurally unstable.

If a government removes a model while the cost of replicating its capability is falling, the government has not raised the barrier. It has mostly raised the incentive.

It has told the market three things at once.

• First, this capability is valuable.
• Second, demand for it will remain unmet.
• Third, any supplier that can provide it outside the government’s reach now has a stronger reason to do so.

That is why the hydra metaphor fits. Cut off one model, and the intervention rewards everyone working on alternatives: open-weight developers, foreign labs, enterprise in-house teams, sovereign AI programs, and rival model companies. The ban does not erase the capability. It advertises the capability.

This is the part of AI policy that conventional debates often miss. The question is usually framed as whether a model is dangerous enough to restrict. That is a legitimate question. Some capabilities may be dangerous. Some access controls may be justified. But the harder question is what the restriction does to the market around the model.

In ordinary regulation, restricting access to a dangerous product can reduce availability. In AI, the intervention can do the opposite if the underlying capability is reproducible and the economics of reproduction are improving.

A hosted frontier model is easy for a government to reach. It sits inside a company. It is accessed through accounts, APIs, contracts, billing systems, and cloud infrastructure. That makes it controllable. It also makes it fragile. A buyer building a critical workflow around that model now has to price in political shutoff risk.

For a casual consumer, that may be an inconvenience. For an enterprise, it is different. If an AI model is embedded in software development, cybersecurity, customer operations, research workflows, or product features, a forced shutdown is not a policy event. It is an outage. A supplier that can be switched off by government order becomes a continuity risk.

That risk changes demand.

Companies will not stop wanting the capability. They will look for versions of it that cannot be removed so easily. That means more interest in open-weight models. Once an open-weight model is released, it cannot be recalled in the same way a hosted model can. It means more interest in in-house models and captive systems, where the capability is consumed internally rather than sold as third-party access. It means more interest in foreign and sovereign supply, especially from governments and companies that do not want critical AI capability dependent on another country’s permission.

Also Read: Indonesia’s AI hiring gap is real, just not 28×

Each restriction therefore sorts demand toward the forms of supply the next restriction is least able to reach.

That is the ratchet. A model can be pulled. A capability, once reproduced across more developers, more jurisdictions, more open systems, and more internal deployments, cannot easily be un-reproduced. Each intervention leaves the field more distributed than before.

This does not mean all AI controls are futile. The strongest objection is real: the bottleneck may not be the model layer. It may be the infrastructure beneath it.

Cheap tokens still require expensive chips, data centres, power, cooling, networking, and semiconductor supply chains. Those constraints are more concentrated than model access. Advanced chips and the tools to make them are physical, scarce, capital-intensive, and easier for states to govern. If the true bottleneck is compute, not model release, then governments may still be able to throttle replication by controlling chips, fabrication tools, cloud access, and power infrastructure.

That is the serious limit to the hydra argument. Model-layer bans may multiply rivals, but chip-layer controls can still slow how fast those rivals grow heads.

Even then, the policy implication changes. The effective control point is not the already-released model. It is the underlying supply chain. Pulling a commercial model after launch is the most visible form of control, but it may also be the least durable. It signals value, disrupts trusted suppliers, and pushes demand toward less controllable alternatives. Controlling compute is harder, more expensive, and geopolitically messy, but it at least targets the layer where scarcity still exists.

This is why the June interventions matter beyond Anthropic or OpenAI.

They show the early shape of a new regime. Frontier AI may no longer be treated as an ordinary commercial product. It may become a permissioned capability, released through government-reviewed access lists, rationed by user category, nationality, sector, or political approval. That may sound safer. In the short run, perhaps it is. But in the long run, a permissioned frontier creates its own counter-pressure.

Also Read: The barrier to AI adoption was never budget, it was knowing where to start

Every company that depends on AI will ask whether its supplier can be turned off.

Every foreign government will ask whether its national security systems can depend on another country’s approval.

Every developer building open alternatives will see stronger demand.

Every rival lab will see proof that the banned capability was important enough to frighten Washington.

That is the paradox. The more dramatically a government signals that a capability is too important to release, the more strongly it tells the world what to rebuild.

The state can pull a model. It can gate a launch. It can force a company to choose between compliance and continuity. But it cannot repeal the economics underneath the technology. Compute-delivered intelligence is getting cheaper. Rival models are catching up faster. Capabilities are moving from single products into distributed ecosystems.

Containment may still work where the bottleneck is physical: chips, power, data centres, semiconductor tools. It is much weaker where the bottleneck is a model that can be matched, fine-tuned, copied, approximated, or rebuilt.

That is the lesson from Fable and Mythos. Washington tried to remove a capability by removing access to a model. Instead, it gave the market a map: this capability matters, demand exists, and whoever can supply it beyond the reach of the next order will be rewarded.

That is how a ban becomes a signal.

And in a technology built on falling costs and fast catch-up, a signal can create more of the thing it was meant to suppress.

—

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 Washington banned Mythos and Fable: It created a hydra appeared first on e27.

Grab Holdings wants investors to look at one number: US$235 million in profit for the second quarter of 2026, a dramatic jump from just US$20 million a year ago. Splashed across the press release, that figure is meant to signal a Southeast Asian super-app finally turning the corner into sustainable profitability.

Peel back the accounting, however, and the story looks considerably less triumphant. Most of that profit swing had nothing to do with rides booked, food delivered, or loans disbursed. It came from a one-off US$307 million gain booked when Grab consolidated Indonesia’s Superbank onto its balance sheet, plus a US$66 million favourable tax movement from recognising deferred tax assets.

Also Read: Superbank under Grab: what the takeover means for Indonesia’s crowded digital banking scene

Strip those non-operating items out, and Grab’s actual operating profit (the money made from running its core deliveries, mobility and financial services businesses) was just US$19 million on revenue of US$997 million. That is an operating margin of under 2 per cent, even as the company touts “record” results and “durable, profitable growth.”

The Superbank gain is a one-time accounting trick, not a turnaround

Grab itself concedes as much in the fine print: “The Superbank remeasurement gain was one-time in nature. We expect our profit for the period in the second half to continue to reflect a degree of variability tied to fair value measurements and other non-operating items.” That is corporate-speak for: don’t expect this profit number again next quarter.

Worse, the US$307 million gain was partially offset by a US$183 million fair value loss on financial assets and liabilities, largely a function of the US$1.5 billion convertible notes Grab issued, whose embedded conversion feature must be revalued every quarter under IFRS rules. Grab even dedicates an entire section of its filing to explaining that this volatility “does not impact Grab’s underlying cash flows or adjusted EBITDA“, a defensive disclosure that suggests the company is bracing for scrutiny over how erratic its bottom line has become, swinging on derivative accounting rather than operational execution.

Buying growth is getting more expensive, not less

CEO Anthony Tan credited an “AI-led strategy” for accelerating on-demand GMV growth to 22 per cent year-on-year on a constant currency basis. But the underlying mechanics tell a more familiar story: Grab is still buying growth with incentives. Total incentives hit US$706 million for the quarter, and on-demand incentives as a proportion of on-demand GMV actually rose 72 basis points year-on-year to 10.9 per cent.

That is not a company weaning itself off subsidies; it is a company spending more per dollar of bookings to keep drivers on the road and users tapping the app, partly because of what it openly calls an “ongoing fuel crisis” hitting driver-partners across the region.

Mobility tells the same tale in miniature. Segment Adjusted EBITDA margin on GMV actually fell 9 basis points year-on-year, because Grab “recalibrated incentive spend towards driver-partners to strengthen supply.” Transactions grew 28 per cent, comfortably outpacing GMV growth of 18 per cent, meaning Grab is discounting harder to keep volumes up, precisely the behaviour investors were told the company had moved past years ago.

Financial services: still losing money, and credit quality is a growing question mark

Grab’s fintech arm remains the weak link. The financial services segment’s adjusted EBITDA improved but stayed firmly negative at -US$15 million for the quarter. More striking is the admission buried in the operating profit commentary: overall operating profit growth was “partially offset by… higher net impairment losses on financial assets mainly driven by Digibank expected credit losses.” In plain English, more borrowers at GXS Bank, GXBank, or the newly consolidated Superbank are failing to repay loans than before.

Also Read: Grab invests in EBOOST as Vietnam’s EV charging race shifts into higher gear

That would be a manageable footnote if the loan book were growing modestly. It isn’t. Gross loan portfolio scaled 197 per cent year-on-year to US$2.3 billion, and even stripping out Superbank’s contribution, it still doubled. Loans disbursed hit an all-time high of US$1.2 billion in the quarter, up 72 per cent year-on-year. Rapid loan growth paired with rising impairments is a textbook early-warning pattern in digital lending, one regulators and credit analysts watch closely, even if it barely rates a mention in Grab’s own release.

Cash generation is actually going backwards

Here is the number that should worry shareholders more than any headline profit figure: Operating cash flow fell US$8 million year-on-year to US$56 million, and adjusted free cash flow dropped a sharper US$39 million year-on-year to just US$73 million for the quarter “due to higher capital expenditures and lower net cash from operating activities.”

A company claiming record profitability should not simultaneously be generating less actual cash than it did twelve months ago. The trailing-twelve-month adjusted free cash flow figure of US$450 million looks respectable on its own, but the quarterly deterioration suggests momentum is stalling just as the profit narrative is meant to be accelerating.

A US$750M buyback, funded by whose cash exactly?

Against this backdrop, CFO Peter Oey announced the Board has authorised a further US$750 million in share repurchases, taking cumulative buyback authorisation to US$1.75 billion since 2024. Grab does sit on US$7.4 billion in gross cash liquidity and US$5.4 billion net, so it can technically afford it.

But the timing invites an obvious question: is returning cash to shareholders the best use of capital for a business whose financial services arm is still losing money, whose free cash flow just shrank, and whose loan book is expanding fast enough to raise credit-quality concerns? Buybacks flatter earnings-per-share and signal confidence to the market; they also happen to be a convenient way to support the share price while the underlying operating margin remains close to zero.

The bottom line

None of this means Grab is in trouble. Revenue growing 22 per cent to US$997 million, 54 million monthly transacting users, and an adjusted EBITDA margin expanding to 16.9 per cent from 13.3 per cent a year ago are genuine signs of a maturing platform. Eighteen straight quarters of Adjusted EBITDA growth is not nothing.

Also Read: Grab posts rare profit, but cash burn and incentive dependence tell a deeper story

But the US$235 million profit figure being pushed to the top of every headline is largely an accounting artefact of the Superbank deal, not evidence of a business that has cracked sustainable, organic profitability. Incentive intensity is rising, mobility margins are slipping, financial services is still in the red with deteriorating credit quality, and actual cash generation fell year-on-year. Investors reading past the press release’s framing will find a company still very much mid-transition, one dressing up an accounting windfall as a profitability milestone while quietly asking shareholders to fund a bigger buyback than ever before.

The post Grab’s US$235M ‘profit’ headline hides a biz still burning cash where it matters most appeared first on e27.

Singapore’s payments industry is getting a new conduct playbook at a time when digital wallets, cross-border transfers and instant payments have become part of daily life for consumers and businesses.

The Singapore FinTech Association (SFA), together with industry players, today launched the Payments Industry Code of Conduct, a voluntary framework that sets out common standards for payment service providers in the city-state. The Code covers areas including pricing transparency, advertising, fraud prevention, card dispute liability, data protection, operational resilience, and anti-money laundering and countering the financing of terrorism controls.

Also Read: Southeast Asia’s fintech apps don’t have a literacy problem, they have a fear problem​

The move comes as Singapore’s payments market matures beyond basic digital adoption. Consumers now expect payment services to be fast, cheap and available across borders. At the same time, regulators and industry bodies are facing a more difficult question: how to preserve trust when payment products are increasingly embedded into apps, platforms and regional business flows.

Unlike a regulation issued by the Monetary Authority of Singapore (MAS), the Code is not mandatory. Instead, payment service providers may assess their own policies, systems and processes against the standards and publicly declare themselves as “Code Adherents”. These declarations are valid for one year and must state the year in which the self-assessment was conducted.

That voluntary structure is important. It gives the industry room to adopt a common baseline without creating a new licensing regime. But it also means the Code’s impact will depend heavily on how many providers sign up, how seriously they conduct their assessments, and whether customers begin to treat adherence as a marker of trust.

What the Code covers

The Code applies to holders of major payment institution licences, standard payment institution licences and money-changing licences, as well as exempt payment service providers, in relation to regulated fiat currency payment services under the Payment Services Act 2019. It does not cover digital payment token services, even where those services are offered by the same provider.

At its core, the Code is an attempt to make payment costs easier to understand before a customer commits to a transaction. Code Adherents are expected to show the full cost upfront, including the principal amount, transaction fees, applicable exchange rate, any mark-up, and the final amount to be transacted.

This is especially relevant for cross-border payments, where consumers and small businesses often compare providers based on advertised fees, only to discover that part of the cost is built into the foreign exchange spread. The Code explicitly discourages “free” or “zero fee” claims where the provider still earns through an exchange rate mark-up, unless that cost is clearly disclosed.

It also takes aim at drip pricing, where mandatory charges are added partway through a transaction. For consumers, this means fewer surprises. For providers, it raises the bar for how pricing must be presented in user flows, advertisements and competitor comparisons.

The Code also states that marketing must not create a false or misleading impression about the cost of a service. Comparisons with competitors must be fair, accurate and capable of being substantiated. Providers should not selectively omit their own costs while highlighting rivals’ fees to suggest savings that may not exist.

Also Read: Singapore’s next payments chapter will be written by AI and tokenised money

SK Saraogi, CEO of Wise Asia Pacific and outgoing Co-Chair of the SFA Payments Subcommittee, said the Code sends a clear message that customers should understand the total cost before making a payment. “A mark-up hidden in the exchange rate is still a cost to the customer and should be displayed transparently,” he said.

Fraud, data and resilience move up the agenda

Pricing may be the most visible part of the Code, but its wider significance lies in how it frames consumer protection as a shared industry responsibility.

Code Adherents are expected to maintain a documented fraud prevention framework. This includes regular risk assessments, transaction monitoring, clear escalation procedures and user education on common scams. They are also expected to participate in or support structured industry-wide initiatives led by SFA, MAS or other bodies where relevant and proportionate to their business model, size and risk profile.

That caveat on proportionality matters. Singapore’s payments sector includes large regional players, specialist remittance firms, card issuers, money changers and smaller fintech companies. A one-size-fits-all compliance model could be costly and impractical. The Code instead tries to establish common expectations while recognising that providers face different levels of risk and operational complexity.

The framework also addresses card dispute liability. For card-based payment services, Code Adherents are expected to adopt liability standards aligned with those applying to banks under the Association of Banks in Singapore Code of Practice. This includes caps on customer liability for unauthorised transactions and clear procedures for reporting lost or stolen cards.

On data privacy and security, the Code requires internal controls, data minimisation and compliance with the Personal Data Protection Act. In the event of a notifiable data breach, providers must notify affected users and the Personal Data Protection Commission as soon as practicable, and within three calendar days of assessing the breach.

Operational resilience is another major pillar. Code Adherents are expected to identify and stress-test critical systems such as ledger and wallet systems, payment gateways, customer-facing application programming interfaces and authentication services. In plain terms, these are the systems that keep money moving, users verified and balances accurate. When they fail, the impact can ripple quickly across merchants, consumers and platforms.

Why it matters beyond Singapore

Singapore has long positioned itself as a trusted fintech hub for Southeast Asia, and payments sit at the centre of that strategy. The city-state is a regional base for global fintech companies, a launchpad for cross-border services, and a testbed for regulatory frameworks that often influence conversations elsewhere in the region.

Across Southeast Asia, payments remain one of fintech’s most competitive and strategically important segments. Digital wallets, real-time payment rails, QR payments and remittance platforms have expanded rapidly, but customer experiences remain uneven. Fees can be opaque, fraud risks are rising, and cross-border payment costs are still a pain point for consumers, migrant workers and small businesses.

Singapore’s new Code does not solve these issues across the region. It is domestic in scope and voluntary by design. Still, it may become a useful reference point for markets trying to balance innovation with consumer protection, especially as payment providers increasingly operate across borders.

Also Read: What stands in the way of fintech growth in Asia?

For Singapore-based providers with regional ambitions, adherence could also become part of their trust narrative when dealing with partners, regulators and enterprise customers in neighbouring markets. In payments, credibility is not just about speed or price; it is about whether users believe the provider will behave fairly when something goes wrong.

Holly Fang, President of the SFA, said payments now touch almost every part of daily life in Singapore, making transparency and protection central to public trust. “For consumers, that means fewer surprises and clearer recourse when something goes wrong. For the industry, it raises the baseline of trust that good businesses are built on,” she said.

The Code will be reviewed and updated regularly as the payments industry evolves. SFA has also said it will welcome new market participants and providers over time.

The key test will come after the launch. A voluntary code can clarify expectations, but it only becomes meaningful if providers adopt it, customers notice it, and the industry treats self-assessment as more than a box-ticking exercise. For now, Singapore’s payments sector has a clearer benchmark for what fairer, more transparent payment services should look like.

The post New Singapore payments code takes aim at hidden mark-ups and misleading “zero fee” claims appeared first on e27.

TikTok Shop is moving beyond the basic pitch of helping merchants sell through short videos and livestreams. In Singapore, it now wants to train a larger slice of the people who make that model work: the sellers in front of the products and the creators who turn browsing into buying.

At its inaugural TikTok Shop Singapore Summit on 30 July 2026, the company announced a fresh set of capability-building programmes aimed at local merchants and content creators. It plans to support and train at least 1,000 Singapore-based sellers by the end of 2027 and more than 3,000 creators by the end of 2026.

Also Read: TikTok Shop is eating Vietnam’s e-commerce market alive

The summit, held as a full-day hybrid event, drew more than 1,500 sellers, creators and ecosystem partners. It also gave TikTok Shop a chance to underline the momentum it says it is seeing locally: in the past year, its Singapore business recorded 1.7 times year-on-year growth in gross merchandise value, a measure of total sales transacted on the platform, and 1.6 times growth in monthly buyers.

The numbers point to a broader shift in Southeast Asian e-commerce. For years, platforms competed largely on assortment, discounts and logistics. Increasingly, the battle is moving to content: short videos, creator recommendations and live selling sessions where hosts demonstrate products, answer questions and close purchases in real time.

From listing products to performing commerce

TikTok Shop’s new seller initiatives are designed for different stages of merchant maturity. The GO LIVE Academy targets new sellers learning the basics of running their own livestream sessions. The Growth Accelerator Programme is aimed at sellers ready to scale through content and live commerce, with incubation and strategic guidance.

For merchants that want to reduce dependence on external hosts, the Merchant Self-LIVE Programme focuses on building in-house live selling teams. A separate Brand IP Accelerator Programme will help sellers with an existing content presence sharpen their founder or brand identity on the platform.

The emphasis is telling. TikTok Shop is not merely asking Singapore merchants to list inventory online; it is asking them to behave more like media operators. Sellers need to script product stories, analyse audience behaviour, manage creators and hosts, and build repeatable content formats.

“We’re seeing a clear shift in how businesses grow on TikTok Shop. Success today is no longer defined simply by having a presence online or treating LIVE selling as another sales channel,” said Leon Koh, Fashion Cluster Lead and Head of Seller Management at TikTok Shop Singapore. He added that stronger businesses are investing in content creation, in-house live expertise and more strategic creator partnerships.

That is particularly relevant in Singapore, where the retail market is small but digitally mature. Local brands often face a ceiling at home, while cross-border platforms and overseas brands compete aggressively for consumer attention. For smaller merchants, live commerce offers a way to differentiate through personality and community rather than price alone, but only if they can execute consistently.

Creators become part of the commerce stack

TikTok Shop is also expanding programmes for creators, reflecting how central they have become to social commerce. Its LIVE Host Academy will train aspiring professional livestream hosts and connect them with sellers. The Affiliate Accelerator will support creators building affiliate-led businesses through live selling and short-form content. The Short Video Programme will help emerging creators develop product storytelling skills and collaborate with TikTok Shop sellers.

The company says live viewership on TikTok Shop in Singapore has increased by 150 per cent year-on-year. That matters because live commerce requires a supply of people who can hold attention and convert it into sales. In practice, this creates new types of digital work: hosts, affiliate creators, video specialists and community builders.

Also Read: TikTok Shop beats Tokopedia to become SEA’s second-largest e-commerce platform

Across Southeast Asia, this creator-commerce layer has become a key competitive front. Indonesia, Thailand, Vietnam and the Philippines have already seen consumers adopt livestream shopping at scale, helped by mobile-first behaviour and high social media usage. Singapore’s market is smaller, but it can serve as a useful testbed for higher-value categories, brand-led campaigns and professionalised creator operations.

The challenge is quality. A flood of low-effort product pushes can quickly erode trust. For TikTok Shop, training creators is partly about expanding supply, but also about making recommendations feel credible enough for consumers to keep watching and buying.

Local brands test a new playbook

Singapore fashion label Young Hungry Free is one example of how merchants are adapting. The homegrown brand, known for bold collections and a community-driven identity, has used TikTok Shop as a channel for content-led selling and live commerce.

Founder and Creative Director Winnie Ong said TikTok Shop had changed how the brand connects with customers. More interesting than the endorsement is the operational shift behind it. According to Ong, Young Hungry Free began with one full-time employee managing live selling sessions alongside other duties. It has since built a dedicated live commerce team of more than 10 people.

That reflects a bigger change in how e-commerce teams are structured. Merchandising and performance marketing are no longer enough. For brands leaning into discovery commerce, content production and live hosting become core capabilities, not side experiments.

TikTok Shop is also positioning itself as a bridge for more traditional businesses. Kim’s Duet, a Singapore brand by coffee company Kim Guan Guan, has used the platform to reach younger consumers while selling traditional local coffee. Nevin Soon, the second-generation coffee manufacturer and management associate, said the company faced a steep learning curve entering live commerce. After joining TikTok Shop’s onboarding and Growth Accelerator efforts, the brand achieved 42 per cent month-on-month gross merchandise value growth in June 2026.

Such examples help explain why social commerce has appeal in Southeast Asia. Many small and family-run businesses are digitally aware but do not have deep e-commerce teams. Platforms that provide training, traffic and creator access can become important growth infrastructure — though they also increase merchants’ dependence on one ecosystem.

A crowded and contested market

TikTok Shop’s push in Singapore comes amid stiff competition. Shopee, owned by Sea Group, remains the dominant e-commerce marketplace across much of Southeast Asia and has invested heavily in livestreaming and affiliate tools. Lazada, backed by Alibaba, continues to court brands and merchants through its regional marketplace infrastructure. Amazon Singapore competes in selected categories with its logistics strength, while Shein and Temu pressure fashion and lifestyle sellers on price and supply-chain speed. On the content side, Meta, YouTube and other social platforms are also trying to capture creator-led product discovery.

This rivalry means TikTok Shop cannot rely on entertainment alone. Its advantage lies in the tight loop between content, recommendation algorithms and checkout. But to sustain that edge, it needs sellers who can produce better content and creators who understand commerce without turning every video into a hard sell.

Also Read: TikTok teams up with Vietnam to power the country’s digital growth

The Singapore summit suggests TikTok Shop is now treating training as part of its market strategy. In a region where e-commerce growth is increasingly shaped by trust, attention and creator influence, the platforms that win may not simply be those with the most products. They may be the ones that teach the most merchants how to sell in a world where shopping starts with discovery.

The post Why TikTok Shop wants Singapore merchants to think like content teams appeared first on e27.

Ask any project manager on a Southeast Asian construction site what eats up their evenings, and the answer rarely involves engineering. It’s paperwork: chasing updates, compiling reports, making sure the right photo is attached to the right ticket.

Ethan Ow knows this frustration intimately. He lived it as a junior executive in 2012, left the industry for tech in 2017, and returned years later to find nothing had changed. Group chats were still buzzing with site updates, but somebody, somewhere, still had to convert that chaos into a proper record,  usually late at night, usually by hand.

Also Read: AI takes centre stage in Singapore’s push for Zero-SIF construction sites

That gap is what Ow set out to close with Wenti Labs, a Singapore-based startup building AI agents that turn WhatsApp messages, photos and voice notes from construction sites into structured reports — no new app, no retraining an entire workforce.

Today, more than 20 paying enterprise customers, including Woh Hup, PentaOcean, Obayashi, and Jacobs, run their reporting through Wenti Labs. The company processes around 20,000 API calls a day and claims to cut a task like a site walk report from an hour down to 30 seconds.

Building inside the chat, not around it

The decision to embed Wenti Labs inside WhatsApp rather than launch a standalone platform wasn’t a branding choice; it was a survival strategy.

“WhatsApp was the obvious starting point because of its ubiquity across Southeast Asia,” Ow explains. The real problem wasn’t a lack of data; it was what happened to it afterwards. Project managers were still pulling information out of chat threads and manually re-entering it into spreadsheets before a report could be filed. Wenti Labs was built to eliminate that step entirely.

“This allows them to continue using a familiar channel while the administrative work happens in the background,” he says.

Why an entire industry got stuck

Construction’s resistance to digitisation is often framed as stubbornness. Ow disagrees.

“A lot of project data is logged in group chats, but the actual digital records are often created later, when the person responsible has time to enter everything manually,” he says. “This delay creates gaps, so different teams can end up working from different sets of information.”

His conclusion is blunt: “I do not think that the industry is resistant to technology. The problem is that many digital tools have asked people to do more admin before they save them any time.”

Inside the pipeline

Before writing any code, Wenti Labs maps out each customer’s workflow — what needs tracking, what a correct report looks like, and where information needs to land. Only then does the team build customer-specific agents using OpenAI’s Responses API, tools, and Agent SDK.

Ow describes a typical scenario: a worker spots a missing safety barricade during an inspection and sends a photo into the project’s WhatsApp group. The model extracts the issue, location, category and criticality, and the system generates a ticket automatically. Once a colleague fixes it and shares evidence in the same chat, the agent closes the ticket, no forms involved.

“The difference is that changes are recorded in real time and the worker does not have to transfer the same information from one platform to another,” Ow says. “Because the output is structured and follows the customer’s workflow, it can be used for official reporting.”

One playbook, many markets

Expanding across Singapore, Malaysia, Indonesia and Vietnam means confronting different documentation standards and languages, a challenge Wenti Labs tackles through configuration rather than a rigid template.

“We tailor the final output to each customer’s local requirements by configuring the required fields and connecting the result to their existing systems,” Ow says.

Some Singapore-based contractors communicate in Mandarin; other customers operate entirely in Vietnamese. Site teams message in whatever language they’re comfortable with, and the agent produces the report in whichever language is required. The company has extended the same approach to heat stress management for customers in Singapore, Australia and Japan, each with distinct regulations.

Whose data is it, anyway

Construction data can include safety incidents, contractor performance and project delays, all flowing through a consumer messaging app. Ow is direct about where the company draws the line.

“We stay true to the principle that all project data belongs to the customer,” he says. “Wenti Labs does not use customer uploads to train or fine-tune a shared model, and our agents can access only the information the customer has authorised.”

Also Read: The dawn of housing abundance: Why AI will collapse construction costs by 90 per cent

Deployment isn’t one-size-fits-all either; the company supports granular access controls alongside region-specific and on-premises storage, letting customers control exactly where their data lives.

Winning over the sceptics

Older, less tech-savvy site supervisors are often assumed to be the hardest group to convert. Ow found the opposite, provided the tool asked nothing new of them.

“Workers of all ages can simply keep doing what they already do,” he says. “From the site team’s point of view, Wenti Labs functions just like another user they can message on WhatsApp.”

The numbers back him up. One customer had previously logged around 30 safety issues a month through a form-based system. After adopting Wenti Labs, that jumped to 300 — not because more accidents were happening, but because roughly 90 per cent of existing issues simply weren’t being recorded before.

“Any new tool can feel unfamiliar at first, but seeing is believing,” Ow says.

The reliability bar nobody talks about

Perhaps the most candid part of the conversation is Ow’s math on reliability. At 20,000 API calls a day, even a model with a 90 per cent success rate would generate roughly 2,000 unreliable outputs daily — “a margin that is not feasible in construction.”

Cheaper open-source models were tested along the way, but the extra engineering needed to hit the necessary consistency wasn’t worth the trade-off. “OpenAI’s reliability has generally kept pace with each update, so we eventually decided it was more practical to stop evaluating every new model and focus that time on solving customer problems,” Ow says.

Codex has also become part of the internal toolkit — not just for writing code, but for quality assurance, monitoring agent outputs across customers and surfacing new edge cases. “A typical sprint used to take two weeks; now we can ship a feature in about two days,” Ow says.

Beyond the site walk report

Safety inspections were the starting point, but other bottlenecks are in the crosshairs too. Equipment certificates, for instance, typically arrive as PDFs that someone manually copies expiry dates from into a spreadsheet. Wenti Labs’ agents now extract that information automatically and flag renewals before they lapse — the same logic applied to consolidating concrete supplier and test lab data for real-time project oversight.

A retention play, not just an efficiency play

Singapore’s construction sector has a well-documented labour crunch, and Ow frames the company’s impact in terms broader than pure productivity.

“It does not help when teams find themselves weighed down by repetitive paperwork instead of the engineering and site work they signed up for,” he says. “Over time, that can push people away from the industry.”

Staying in its lane

Despite interest from farming, manufacturing and shipping, Ow has no plans to turn Wenti Labs into a generic tool for every industry.

“Wenti Labs’ domain knowledge lies in construction,” he says, pointing to legal-AI platform Harvey AI as the model he’s chasing: deep, not broad.

Also Read: The vision-based shift: Transforming construction safety with AI

The ambition stretches beyond report generation altogether — Ow wants Wenti Labs to become something closer to an operating system for construction projects, quietly connecting chats, emails, documents and existing software into a single, coherent record of what’s actually happening on the ground.

For an industry that has spent decades logging its most important information in group chats and losing track of it soon after, that would be less a feature than a fundamentally different way of working.

The post The 30-second report: How one startup is killing construction’s paperwork problem appeared first on e27.

One of my jobs in what I came to think of as a corridor business did not look like one at the time. It was in the years before COVID-19, at China Business Network, a small but meaningful consultancy in Beijing, where my role was to help governments attract Chinese visitors, which gave me an actual front-row seat to what I think of as modern commerce.

This was the era when Chinese travellers took 155 million trips abroad in a single year, and spent US$255 billion doing it, reported McKinsey back then. It was amazing to witness, because the world was hungry for a share of it, and governments from Qatar, Bahrain, Luxembourg, the Ivory Coast, Sudan, American tourism boards and destinations, and many more came to our door, all with the same request: help us make the Chinese visitor comfortable in our country, comfortable enough to stay and spend, and show us how to, before our neighbours figure it out.

Our job was to move them to the front of that queue by means of marketing, promotion, media, and consulting advisory services. We secured them top billing at Beijing’s flagship tourism events, had their destinations promoted to the Chinese public, media appearances, and partnerships with local businesses. Needless to say, they routinely stole the show, and the partnerships we grew lasted many, many years.

In hindsight, those governments rehearsed a relationship with a rising economic power, learning its holidays, payment applications, its hospitality codes, and its way of building trust. A tour bus was a prototype to what would pave the way to something greater, a bond I saw take place with every market we served: people flows predict capital flows. Governments signed agreements constantly with China, but only some became actual corridors; now, to know which ones will shape the future, watch the arrivals hall at airports.

The sequence has a shape

Credit: Embassy of the People’s Republic of China in Singapore

Signatures on paper come first; for instance, ASEAN and China signed their framework trade agreement in 2002, ASEAN reported, with Beijing granting approved-destination status country by country, and Chinese delegations initiated agreements on nearly every state visit then. But we all know early paper is only permission, not proof of anything, and a lot of signed agreements never made it past the initial stage. What made the others succeed was how they moved, the number of their visitors, the small adaptations, Mandarin signage, familiar payment rails, staff trained to understand what Chinese guests actually wanted, and with that, familiarity was quietly building between parties.

Familiarity allowed deep architecture to consolidate, and from that, trade under the agreement grew more than fivefold. ASEAN overtook the EU as China’s largest trading partner in 2020, Nation Thailand reported, with two-way trade reaching a record US$984 billion in 2024, while the region has been the top FDI recipient among developing regions for four consecutive years, with manufacturing FDI climbing nearly 150 per cent to US$44 billion, which led to CAFTA 3.0 arriving only in late 2025, a quarter century after the framework.

Also Read: Southeast Asia in the 2026-2030 world order: Trade, chips, AI, and capital

So when we say it’s signatures first, people, second, and they’re the real test. Then comes capital, and only then deep architecture that allows existing flows to run. Nothing de-risks a market like millions of ordinary encounters involving tourists, students, and pilgrims are a distribution of due-diligence networks that predict which signed agreement truly comes alive, while others vanish in the halls they were signed in.

Now, watch the same tape run one more time

Credit: Saudi Embassy in Singapore

Apply the rule today, and something jumps out: Asia is emerging as a top choice for Gulf travellers, with destinations from Singapore, Bangkok to Phuket, Penang, and Bali adapting through halal-friendly services and expanding air links with the Gulf. In the Global Muslim Travel Index 2025, Singapore leads every non-OIC destination while Malaysia holds the top position among OIC countries: Southeast Asia is where the Gulf goes on holiday, and notice the symmetry here: some of the same Gulf states that once courted Chinese travellers have become the source market everyone else now adapts for.

This said, unlike the China corridor, this one has a cultural floor, with Indonesia holding the world’s largest Muslim population, halal is natively ingrained, not an accommodation, and a Gulf family lands in Southeast Asia already halfway to comfortable, so a corridor there that serves this public should run the sequence faster.

Now, look one step behind the travellers

Middle Eastern sovereign wealth funds manage more than US$5.6 trillion in assets, a figure projected to reach US$8.8 trillion by 2030, wrote Foreign Policy, increasingly run from offices in Asia rather than London now. This is one of the reasons why the Gulf is converting a finite oil endowment into permanent stakes in the world’s fastest-growing consumer markets while hedging deliberately against the unpredictability of Washington’s tariffs, thrown here, there and everywhere.

Asia House expects GCC trade with emerging Asia to climb from US$450 billion in 2023 to US$680 billion by 2030, and the whole deployment has already begun with Abu Dhabi’s ADIA partnering with the Indonesia Investment Authority on toll roads, joining the consortium that acquired Malaysian Airports. While in May 2025, Kuala Lumpur hosted the first-ever ASEAN-GCC-China trilateral summit, here are three blocs representing a combined GDP of US$24.87 trillion and some 2.15 billion people.

Also Read: As global trade fragments, Southeast Asia must build leverage, not just attract investment

I must add, if the China sequence I spoke about took two decades, endless tour buses, and trade partners to take shape, the Gulf-Southeast Asia way is being paved; it is still in its early days, and that’s exactly when positioning is still cheap and highly rewarding, while other regions are still figuring things out.

Northeast Asia is already running the experiment

Credit: Saudi Gazette

Adaptation is a choice, not an accident of geography, and Korea and Japan are running the experiment on behalf of Southeast Asia. It needs to learn from the following two neighbours, and how their diverging results preview what happens when a lead is tended, and another is left alone.

South Korea’s tourism authorities have run a systematic Muslim-friendly program for over a decade, with four-tier restaurant classification, prayer kits for hotels, guide training, and incentives for prayer rooms, and what makes the program remarkable is the anti-Muslim sentiment wind that has been sweeping the country.

South Koreans had years-long conflict over a mosque in Daegu, reported the Korea Herald, where opponents to the project placed pig heads outside the construction site, and a 2018 petition against Yemeni asylum seekers that drew some 700,000 signatures. South Korea’s tourism professionals looked at where travellers are going, and built the pavement anyway.

Japan is the sharper, and quite unconventional, lesson. It made a move very early, and reached an all-time high of 3rd place among non-OIC destinations in the 2019 index, but the country’s efforts remained fragmented. Japan’s tourism authority acknowledges that there is no central agency that handles halal accreditation, and by 2023, the country slipped to the 6th position while Singapore held the non-OIC top spot in 2025. In short, Japan’s early adaptation decayed as soon as it stopped moving forward.

Why people always come first

I learned the trust half in person. I am from northern France, bordering Germany, where business means punctuality, agendas, and follow-through, and I’ve witnessed that in some parts of the world, trust translated into dinners, over months, through relationships preceding any transaction. I began closing deals only when I stopped selling and started understanding and adapting.

That gap now sits in the middle of the Gulf-Southeast Asia corridor mentioned, not only through language alone, but the deeper kind that displays how trust is established before anyone shows a number, and what silence means in Riyadh versus Jakarta. Every Gulf family in Langkawi, every Emirati student in Kuala Lumpur, every new direct flight quietly closes that gap, and the visitors are the corridor’s earliest infrastructure that shapes the future of the bond.

Also Read: The hidden engine driving Bitcoin price action that most retail traders ignore: Open interest

The caveat and how founders can use the rule

All in all, the GCC remains only ASEAN’s seventh-largest trading partner, and the bloc-level ASEAN-GCC free trade agreement is still a feasibility study, not a signature. The good news is Malaysia went the farthest into the relationship by launching its own FTA negotiations with the GCC, and this is where founders can leverage existing opportunities. 

Treat travel data as market intelligence: arrival numbers, flight routes, and Muslim-friendly rankings are public leading indicators almost nobody in technology reads. Build cultural awareness and readiness before the wave, as the businesses that adapted early to Chinese visitors did, because the same window is open now for Gulf customers across hospitality, retail, health, education, and digital services, and sell to the Gulf rather than merely raising from it.

Where Southeast Asia fits

Back at China Business Network, the governments that won Chinese visitors were never the ones with the biggest budgets; they were the ones willing to learn how the other side built trust. They were the ones who kept coming back to Beijing, learned what comfort meant to a Chinese guest, from the food on the table to the pace of a negotiation, and built it before their rivals did.

Malaysia’s Anwar Ibrahim has long maintained that neutrality is the source of ASEAN’s centrality, and the arrival halls suggest the market agrees. In a world where great powers force everyone to choose, the scarcest asset is a place that forces no one, and that is what keeps Southeast Asia’s terminals full. The pattern has run through the region twice, first with China, now the Gulf region, which is currently after Singapore, Malaysia, and Thailand.

Great treaties follow the flows, and corridors get built in a way that no summit can legislate, with halal kitchens a Bangkok hotel adds, the Arabic-speaking staff at a Kuala Lumpur clinic, the direct flight that turns a Gulf family’s holiday into a habit. Millions of small welcomes like these are the region’s real endowment, so when the ASEAN-GCC agreement moves from speeches to signatures, the advantage will belong to those who spent years studying their markets, language, and habits.

—

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 Tourists first, trade later: Why Southeast Asia’s next boom is coming from the Gulf appeared first on e27.

For every startup that celebrates a successful Series A funding round, dozens quietly discover that raising capital is the easier part.

The real test begins after the money arrives, when investors expect not just growth but predictable, repeatable execution. It is at this stage that many founders realise they are no longer building a product, but they are building an institution.

India’s startup ecosystem has matured enough to reveal a pattern. Companies that successfully transition from Series A to growth-stage businesses rarely win because they raised the most capital. They become bigger because founders reinvent themselves as the company evolves.

The founder who excels at discovering product-market fit is not automatically equipped to manage a 300-member organisation, multiple business lines, international markets and board expectations. Scaling requires learning a completely different set of skills.

—

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 Founders’ playbook: What it really takes to scale beyond Series A appeared first on e27.

For decades, we’ve taught financial literacy as one of life’s most important skills. Save before you spend. Build an emergency fund. Invest early. Understand compound interest. Avoid unnecessary debt. Those lessons remain just as relevant today as they were a generation ago.

But I believe future generations will need to master another form of literacy alongside them: AI literacy. Not because artificial intelligence is the latest technological trend, but because it is rapidly becoming one of the most powerful forms of economic leverage available.

The conversation around AI has largely centred on productivity. Businesses talk about doing more with less, while employees worry about whether AI will replace their jobs.

I think we’re asking the wrong question.

The real value of AI isn’t simply helping us work faster. It’s giving individuals greater control over how they think, build, earn and create opportunities.

Just as financial literacy changed the way people managed money, AI literacy may change the way people create it.

Financial literacy helps us manage money, AI literacy helps us create leverage

Financial literacy has always been about making better financial decisions.

Should you save or invest? Should you buy a property or continue renting? Should you build a business, start a side hustle or remain employed? Should you diversify your investments or focus on growing your company?

There has never been a single correct answer because everyone’s financial goals, responsibilities and appetite for risk are different. That’s what makes financial literacy so valuable. It equips people to make informed decisions based on their own circumstances.

I believe AI literacy is evolving in much the same way. Many people think AI is about replacing work. I see it as helping people make better decisions.

Imagine having a personalised thinking partner that can help you evaluate a business idea, compare different income strategies, identify blind spots, challenge your assumptions or explore opportunities you hadn’t considered. Not because AI knows the “right” answer. But because it can help you think more broadly and more objectively.

Whether someone chooses to become an entrepreneur, build a side hustle, invest in the stock market or remain in full-time employment, these are deeply personal financial choices.

AI won’t make those decisions for us. But it may help us make better ones.

Also Read: Southeast Asia in the 2026-2030 world order: Trade, chips, AI, and capital

AI literacy isn’t about learning prompts

One of the biggest misconceptions I encounter is that AI literacy simply means learning how to write better prompts.

That’s like saying financial literacy is knowing how to use a calculator. The tool isn’t the skill.

Real AI literacy is understanding how to redesign the way you think and work. It’s recognising which tasks require uniquely human judgement and which repetitive processes can be delegated to AI. It’s learning how to collaborate with technology rather than compete against it. It’s thinking in systems instead of individual tasks.

More importantly, it’s about understanding leverage.

Someone who knows how to use AI strategically doesn’t simply finish work faster. They create capacity. Capacity to build another income stream. Capacity to launch a business. Capacity to spend more time with customers. Capacity to solve bigger problems.

That’s a very different outcome from simply becoming more productive.

The confidence gap is often bigger than the technology gap

Through the workshops and programmes I run for entrepreneurs and professionals, I’ve noticed something fascinating. Many participants begin with exactly the same sentence. “I’m not a tech person.”

A few weeks later, those same individuals are building automations, validating business ideas, launching digital products and creating workflows they never imagined possible.

The technology didn’t change. Their confidence did. The biggest barrier wasn’t technical ability. It was the belief that AI was only for developers or engineers.

Once that mental barrier disappears, people begin asking different questions. Instead of asking, “Can I do this?” they start asking, “How could I build this?” That shift in mindset may be one of AI’s greatest contributions.

AI isn’t making us think less. It’s changing where we think.

One criticism I hear regularly is that AI will make people intellectually lazy. Used carelessly, perhaps. But the same argument could have been made about calculators, spreadsheets or search engines. Technology has always changed how we work.

The real question is whether it allows us to think at a higher level. I believe it does.

Instead of spending hours rewriting documents, summarising meetings or performing repetitive administrative tasks, AI allows us to redirect our thinking towards strategy, creativity, relationships and decision-making.

It’s not about using less of our brain. It’s about using more of it where it matters most. AI shouldn’t replace thinking. It should elevate where our thinking creates the greatest value.

Also Read: Bring uncertainty out for lunch: Why leadership may need a new management discipline in a changing world

Why AI education should become mainstream

Financial education exists because society recognises that understanding money leads to better outcomes. I believe AI education deserves the same treatment.

Not everyone will become an AI founder. Not everyone will build a startup. But almost everyone will need to understand how AI affects their career, business or future earning potential.

Future generations shouldn’t only learn how to budget, save and invest. They should also learn how to evaluate AI-generated information, collaborate with AI responsibly, automate repetitive work and use AI to solve meaningful problems.

These aren’t simply technical skills. They’re economic skills. Because increasingly, understanding AI won’t just influence how we work. It will influence how we earn.

The next evolution of financial literacy

Financial literacy isn’t becoming less important. If anything, it matters more than ever. Understanding how to manage money will always be fundamental.

But managing money is only one part of building financial security. Creating opportunities is the other.

AI gives individuals something previous generations rarely had access to: personalised leverage. The ability to explore business ideas, validate opportunities, automate execution, test different strategies and learn at a pace that was previously impossible without significant resources.

For the first time, sophisticated guidance is becoming accessible to almost anyone with an internet connection. That’s why I don’t see AI literacy replacing financial literacy. I see it becoming part of it.

Just as previous generations learned about budgeting, saving and investing, future generations may also learn how to leverage AI to build businesses, create side hustles, automate income-generating systems and make more informed financial decisions.

Not because AI guarantees success. But because it gives more people access to the tools that make success possible.

Also Read: The pitch you never gave: What AI tells buyers about your startup

The next chapter of financial literacy

Every major technological shift changes the skills society values.

The internet made digital literacy essential. Smartphones transformed how we communicate. Cloud computing reshaped how businesses operate.

AI is different because it doesn’t simply change how we access information; it changes how we think, create and execute.

That’s why I believe AI literacy is becoming as important as financial literacy. Not because everyone needs to become an AI engineer or build the next unicorn startup, but because understanding how to leverage AI will increasingly shape a person’s ability to create opportunities, generate income and make better financial decisions.

Financial literacy teaches us how to manage money. AI literacy teaches us how to create leverage.

Together, they give people something far more valuable than either skill alone: the ability to build a future on their own terms. Because in the years ahead, the greatest advantage may not belong to those who work the hardest. It will belong to those who know how to combine human judgement with AI leverage.

—

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 AI literacy may become the new financial literacy appeared first on e27.

Every few months, a new platform promises to hand retail traders the same edge that institutional desks have spent decades building. Plug in the AI, sit back, let it run. The pitch has become so familiar it barely registers anymore.

I’ve been trading for thirty years and made every mistake in the book. What I’ve learned, slowly and sometimes expensively, is that the gap between a good trader and a losing one is rarely about access to better data. It’s about what happens between receiving a signal and deciding what to do with it. That gap is where discipline lives, and it’s where most retail traders come unstuck.

AI cannot close that gap by excluding the human from the equation. Used well, though, it can do something more valuable: make the human in that equation better.

Why the losses keep happening

The numbers on retail trading outcomes have been remarkably consistent for decades. India’s markets regulator, SEBI, spent three years tracking live trading data and found that 93 per cent of retail futures and options participants ended in the red. A 27-year dataset of eight million traders, covering every major market cycle from 1998 to 2025, showed failure rates holding steady between 74 per cent and 89 per cent, regardless of conditions.

What’s striking about that data is not the scale of the losses. It’s the consistency of how they happen. A period of early success, then overconfidence, then the familiar spiral: positions held too long, stops moved in the wrong direction, winners sold off before they’ve run.

These aren’t analytical failures. They’re behavioral ones. Giving a trader faster AI tools doesn’t fix them. If anything, it gives them a faster way to repeat the same mistakes.

Also Read: Finance doesn’t have a math problem, it has an ego problem.

The checklist I kept on my desk

When I started trading, computers were new enough that I kept a physical checklist next to the screen. Five items. Every single one had to be checked before I put on a trade. Looking back, it was the thing that saved me from myself more than any indicator or strategy ever did.

The same principle sits at the heart of how I think about AI in trading. A well-built system should process a large number of combinations and discard most of them. The job is to surface only the moments when multiple independent signals simultaneously point in the same direction. When that happens, the notification fires. When it doesn’t, nothing happens, and that silence is the system doing its job.

A 2025 study in the IUP Journal of Accounting Research found a negative correlation between AI tool adoption and loss aversion: traders who regularly used AI-based platforms were measurably less prone to premature exits and fear-driven decisions. The tools were improving the conditions for decisions, not replacing them.

The problem with more

The instinct when building or using AI for trading is to maximise: more signals, more alerts, more data across more screens. The logic seems sound. More information should mean better decisions. In practice, it usually means the opposite.

Crypto trading is the clearest case study. The market never closes. It reacts to a tweet, a regulatory rumour, a shift in sentiment elsewhere: all without the session structure that gives forex or futures traders natural stopping points to reexamine. Traders who manage structured markets well often find crypto genuinely difficult, not because the basic mechanics are more complex, but because there’s no closing bell to interrupt the emotional momentum of a bad run.

Also Read: The fatwa lag: How AI is overtaking the system designed to govern Islamic finance

Keeping accountability where it belongs

The version of AI-assisted trading that actually works looks like this: the technology handles the analytical work that humans genuinely can’t do well at speed. Scanning across instruments, monitoring correlations, tracking whether the risk profile of an open position has shifted. That work is relentless and emotionless, which makes it well-suited to a machine. Carrying it mentally through a full trading session tires a person, and tired traders make worse decisions.

What the machine shouldn’t own is the outcome. The decision to trade, the parameters around risk, the wider context of why this position makes sense now: those have to stay with the human. The moment that accountability is fully delegated, you’ve also removed the last check on the system’s own blind spots.

A boring trader is a wealthy trader

A boring trade is the one that builds wealth. It is the six-hour trade where all you do is adjust a stop every sixteen minutes to lock in more profit. No story. No social media post. But it compounds.

AI’s role is to make that boring trade easier to hold: removing the emotional pressure to act when the right move is to wait, and providing the structural prompts that keep a trader in a position long enough for it to work. In that way, it supports patience rather than replacing it.

Retail traders now have access to analytical resources that were, until recently, exclusive to institutional desks. That is a meaningful shift. But access to better tools is not the same as better judgment. Judgment, about risk, about context, about when to sit on your hands, remains the trader’s responsibility.

Use AI for the things it does better than you. Keep the decisions that require context and stewardship in your own hands. That division of labour, more than any single signal or strategy, is what makes trading sustainable over time.

—

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 How to use AI to become a better investor appeared first on e27.

Huawei Cloud has launched its agentic AI infrastructure in Thailand and opened beta testing for CodeArts Agent, a software development tool that uses AI agents to generate code, answer engineering questions and automate parts of the development process.

The announcements were made at Huawei Cloud Summit Thailand 2026 in Bangkok, where the company gathered government officials, enterprise leaders, partners and developers to discuss how cloud and artificial intelligence can support the country’s digital economy.

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

For Thailand, the timing is significant. Like many Southeast Asian markets, the country is trying to move AI from policy statements and pilot projects into everyday use across government services, banking, education and industry. That shift depends less on chatbots alone and more on the underlying infrastructure: local cloud capacity, secure data environments, developer tools and computing systems that can run AI workloads at scale.

Huawei Cloud is positioning its latest launch around that need. The company said its Agentic Infrastructure, now available in Thailand, is designed for building and deploying AI agents — software systems that can plan, reason, use tools and perform tasks with varying levels of autonomy.

Building for the next AI layer

The term “agentic AI” has quickly become one of the industry’s most used phrases, but its practical meaning is straightforward. Instead of simply responding to a prompt, an AI agent can break a task into steps, remember context, interact with other systems and keep working towards an objective. In an enterprise setting, that could mean helping a bank generate and test software, assisting government officers with document workflows, or supporting an HR platform that creates personalised learning plans.

Huawei Cloud’s new infrastructure is aimed at making those use cases easier to deploy. According to the company, Agentic Infrastructure supports efficient token generation, unified scheduling of general-purpose and AI computing resources, continuous learning, and secure autonomous operations.

Surasak Wanichwatphibun, CTO of Huawei Cloud Thailand, introduced four main components under the infrastructure. These include UnifiedBus-based AI Cluster Service, or AICS, which is designed to improve token generation efficiency; Agentic Memory Storage Service, or AMS, which offers petabyte-scale memory storage for long-horizon agent tasks; AgentSphere, a secure runtime environment for AI agents; and CCE VolcanoNext, which allows general computing and AI computing resources to be scheduled together.

These details matter because enterprise AI does not run on enthusiasm alone. AI agents need access to memory, compute, orchestration and security controls. If those layers are weak, companies may end up with impressive demos that are difficult to move into production.

In Southeast Asia, where many businesses still operate with fragmented legacy systems, the challenge is even sharper. Enterprises want AI tools that can connect to existing workflows without creating new security risks or runaway infrastructure costs. Cloud providers are therefore competing not only on model access, but also on whether they can provide the full stack needed to run AI reliably.

CodeArts Agent enters open beta

The second major announcement was the Thailand open beta testing launch of Huawei Cloud CodeArts Agent. The tool combines an integrated development environment, coding models and autonomous development capabilities.

For developers and enterprise technology teams, CodeArts Agent supports project-level code generation, code completion, research and development knowledge Q&A, and unit test case generation. Huawei Cloud said the product also applies Specification-Driven Development, a method that links software requirements more closely to the coding and delivery process in order to maintain quality from the earliest stage of a project.

The release also introduces an “Agent Team” mode, where multiple AI agents can be assembled to work together on development tasks at the same time. In theory, this could help software teams reduce repetitive work and accelerate product delivery.

Also Read: 4 ways of agentic AI applications in marketing

That is a message likely to resonate in Thailand and across the region. Southeast Asian companies are digitising quickly, but technical talent remains unevenly distributed. Banks, retailers, logistics firms and public agencies are all under pressure to ship digital products faster, yet many teams face shortages of experienced engineers. AI coding tools are unlikely to replace developers wholesale, but they can change how engineering teams allocate time — shifting routine tasks such as boilerplate code, documentation and test generation to AI assistants.

Still, adoption will depend on trust. Developers need to know whether generated code is secure, maintainable and compatible with internal standards. Enterprises also need clarity on where source code is processed and how proprietary information is protected. That makes Huawei Cloud’s emphasis on security a central part of the launch, not a side note.

Security becomes part of the AI pitch

Huawei Cloud said it has upgraded its security services in two areas: protecting AI systems and using AI to strengthen cyber defence.

Its model lifecycle security solution covers AI infrastructure, training data, model inference and agent applications. The company also said it offers dedicated security zones for enterprises concerned about data sovereignty and privacy. These zones allow customers to manage their own encryption keys while preventing platform administrators from accessing customer data.

Huawei Cloud added that it uses software-hardware integration and hardware acceleration to maintain encryption performance. It has also introduced Data Capsule technology, which ensures that data can only be used within authorised environments and becomes invalid if moved outside a designated security zone.

This reflects a broader concern in the region. Governments and regulated industries in Southeast Asia increasingly want the efficiency of public cloud and AI, but not at the expense of control over sensitive data. Thailand, Indonesia, Malaysia and Vietnam have all been paying closer attention to data localisation, cybersecurity and digital sovereignty as cloud adoption deepens.

At the summit, Thailand’s Ministry of Digital Economy and Society was cited as emphasising the need to accelerate digital transformation through national AI policies, public-sector adoption and stronger public-private collaboration. NECTEC also presented a government AI case study covering AI infrastructure, AI platforms, chatbots and intelligent assistants to support officials and improve citizen services.

A crowded cloud race

Huawei Cloud is not alone in trying to capture Thailand’s AI and cloud demand. Global hyperscalers such as Amazon Web Services, Microsoft Azure and Google Cloud are expanding their Southeast Asian footprints, while Alibaba Cloud and Tencent Cloud have long targeted Chinese-linked enterprises and digital businesses in the region. Local and regional infrastructure players, including data centre and connectivity providers, also remain relevant for customers with strict latency, compliance or hybrid-cloud requirements.

This competition is pushing cloud vendors to localise more deeply. It is no longer enough to sell compute and storage from afar. Providers are building local data centres, forming public-sector partnerships, courting developers and packaging AI tools for specific industries.

Huawei Cloud says it was the first international public cloud vendor to establish local data centres in Thailand and now operates three Availability Zones in the country. The company also says it serves more than 40 government agencies and thousands of enterprises in Thailand. Citing Gartner, Huawei Cloud said it ranks third by revenue in Thailand’s Infrastructure-as-a-Service market.

The summit showcased use cases from Thai organisations, including AI platforms and intelligent assistants for the public sector, AI coding and large language models in banking, and AI-powered learning and skills development platforms for HR.

Also Read: The coming identity crisis of agentic AI

For Huawei Cloud, the Thailand launch is part of a wider attempt to move up the cloud value chain, from infrastructure provider to AI operating layer for enterprises. For Thai organisations, the bigger question is how quickly agentic AI can move from a conference-stage concept to a dependable tool inside real workflows.

That transition will require more than powerful models. It will require local infrastructure, stronger governance, developers who understand the tools, and enterprises willing to rethink how work is done. Huawei Cloud is betting that Thailand is ready for that next step.

The post Huawei Cloud bets on Thailand as enterprises move from AI pilots to production appeared first on e27.