The October 28 crypto session clearly shows how two forces can shape prices at the same time. Bitcoin gained 0.59 per cent to US$80,576.90 over 24 hours. The total crypto market cap rose 0.64 per cent to US$2.7 trillion. My view is that the headline numbers hide an important split. Bitcoin moved with traditional markets ahead of the Federal Reserve decision.

The wider crypto space also received a specific lift from Ripple’s institutional partnerships. That split matters more than the small daily gain because it shows Bitcoin trading as a macro instrument while select altcoins begin to respond to their own fundamental news.

The primary force behind Bitcoin was a market-wide beta lift. The largest digital asset rose in sync with equities and gold as traders positioned themselves before the Fed announcement. Bitcoin showed a 91 per cent correlation with the S&P 500 and a 92 per cent correlation with gold. The Nasdaq jumped 1.2 per cent as U.S. stocks rallied into the decision. The SPY correlation stood at 0.9074 and the gold correlation at 0.92271. This behaviour points to a rate-sensitive asset responding to interest rate expectations and liquidity conditions.

The data showed no clear coin-specific trigger. Spot volume fell 15.6 per cent and failed to confirm a strong bullish breakout. Derivatives activity stayed muted, with funding rates positive but low. Twenty-four-hour liquidations decreased to US$32.84 million. I read this as portfolio adjustment rather than aggressive long positioning in Bitcoin. That makes the gain fragile. A hawkish Fed surprise could erase it quickly.

The wider market found a different primary force. Ripple announced two developments on October 27 that changed sentiment for XRP and its new stablecoin. BNY Mellon added XRP and Ripple’s RLUSD stablecoin to its custody platform. Robinhood entered a strategic partnership to use RLUSD for institutional trade settlement and treasury operations. These steps directly address institutional custody and liquidity needs. They validate Ripple’s utility in traditional finance and lift sentiment across assets in its orbit.

BNB also performed well. BNB Standard Corporation trades on the Nasdaq Capital Market under the ticker symbol BNC and provides public market equity exposure to the BNB cryptocurrency. The Altcoin Season Index rose 5.56 per cent to 57, which signals a nascent rotation of capital from Bitcoin into select altcoins. Bitcoin dominance stood at 60 per cent. This is a meaningful shift. While Bitcoin mirrored the S&P 500 and gold, another development pulled capital toward assets with a clear institutional story.

Also Read: 3 scenarios for October 28: Which one breaks Bitcoin’s US$80,000 resistance

A supportive macro backdrop helped both stories. The S&P 500 gained 0.6 per cent, and the Nasdaq gained 1.2 per cent. The wider crypto space showed a 91 per cent correlation with the S&P 500 and a 93 per cent correlation with gold. Those links suggest a shared liquidity move rather than isolated crypto strength.

My perspective is that this combination is healthy. Bitcoin has become a short-term macro proxy. That role brings institutional legitimacy and liquidity. It also imports volatility from traditional markets. Altcoins now have room to trade on their own news flow. The Ripple partnerships involve named institutions with real custody and settlement functions. That kind of development can withstand a Fed-induced volatility spike better than a pure-beta trade.

The near-term Bitcoin picture is balanced but conditional. Bitcoin is testing the lower bound of its recent range. Key support sits between US$79,380 and US$80,000. Resistance rests near US$81,800 to US$82,500. The 50-day exponential moving average sits near US$81,873. Significant sell-side liquidity rests in the US$82,300 to US$82,500 zone.

A daily close above US$81,800 would signal bullish momentum. A break below US$79,380 would open a path toward US$77,500. The Fed policy statement and press conference later today, October 28, will decide the next directional move. After the announcement, Bitcoin needs to reclaim and hold above its 50-day exponential moving average. Without that step, the gain remains a macro bounce rather than independent strength.

Also Read: Bitcoin’s macro reset: Reclaim US$80,000 before the Fed speaks?

The wider crypto valuation faces its own technical test. The market cap is testing the 78.6 per cent Fibonacci retracement level at US$2.73 trillion, which now acts as near-term support. Holding above that level could set the stage for a retest of the US$2.81 trillion level, the 50 per cent Fibonacci mark.

A hawkish Fed tone or a break below support could reverse gains and lead to a test of the swing low at US$2.68 trillion. The Fed decision arrives on October 28 at 18:00 UTC. The key question is whether momentum from Ripple’s institutional breakthroughs can withstand volatility from the Fed policy signal. I will watch sustained on-chain activity for RLUSD and XRP. I will also watch whether other major custodians follow BNY Mellon’s lead. Bitcoin dominance at 60 per cent remains another useful gauge of rotation.

My overall view is cautiously optimistic, but the reasons differ from the headline numbers. Bitcoin’s gain reflects a macro-led bounce with the broader market, not independent strength. That makes it vulnerable to a single-event risk. The Ripple development provides a sector-specific trigger that could sustain altcoin outperformance even if Bitcoin stumbles. The 92 to 93 per cent gold correlation across both stories suggests crypto currently trades as a rate-sensitive asset class. That link brings liquidity and institutional legitimacy. It also brings imported volatility.

The smart approach for the next session is to watch two questions at the same time. Can Bitcoin reclaim and hold above US$81,873 after the Fed announcement? Does the Ripple story generate sustained on-chain activity for RLUSD and XRP? If both answers are yes, the market can build on this session. If Bitcoin breaks below US$79,380 while altcoin flows remain strong, the split between macro beta and fundamental triggers will define the week.

—

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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When Meta switched on Muse in the US last month, it let its new personal AI agent wander into Amazon.com unannounced. Twelve days later, Amazon showed it the door, complaining that the bot did not identify itself and that nobody had asked permission.

In Singapore, Meta has brought the hosts along.

Also Read: Zuckerberg’s Muse Charm doesn’t care about your ad budget

Muse, which Meta pitches as an agent that does the work rather than merely answering questions, went live in the city-state today on iOS, Android and muse.ai. It arrives with eight local launch partners, or “connectors” in Meta-speak: Shopee, Grab, Singapore Airlines, Agoda, ShopBack, Klook, Trip.com and Lazada. Together they span shopping, groceries, dining and travel, which covers most of what a Singaporean does on a phone between meetings.

What Muse can actually do here

Connectors let the agent act inside services people already use. Beyond the eight local partners, Singapore users can plug Muse into Gmail, Google Calendar and Instagram to manage email, juggle schedules and draw on saved posts.

For instance, ask Muse to pick up a pack of newborn nappies and the ingredients for fried rice, and it asks clarifying questions, builds a GrabMart basket, checks the total and delivery time, then waits for your approval. Ask for the cheapest return flight to Seoul, and ShopBack compares fares across booking sites after cashback. Klook’s pitch involves friends in town for December’s BTS concert, with Muse suggesting and booking activities in the same conversation.

The fine print matters, though. Meta says connector features will roll out progressively, with some experiences arriving only after launch, and it does not say which ones. Day one may be thinner than the partner list suggests.

Muse is free for most uses, with paid tiers for heavier users. In the US, those tiers reportedly cost US$20 and US$100 a month; Meta has not disclosed Singapore pricing. A version for AI glasses is “coming soon”.

Why the platforms said yes

The partner quotes are mostly what you would expect. Grab’s chief product officer Philipp Kandal talks about linking conversational AI with “real-world action”. Agoda’s Matteo Frigerio claims his company is the first digital travel platform globally to connect its own AI travel assistant to Muse.

The most candid line comes from ShopBack CPO Justin Lee. Agents get things done, he argues, but the web was built for people to read rather than for agents to act on, hence the need for connectors. It is the same problem startups such as Singapore-founded Lightsage are raising money to decode.

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

Read that again from ShopBack’s side. A cashback business lives in the gap between shopper and merchant. An agent that can compare prices on its own threatens to route around such middlemen entirely. Becoming a launch connector is partly insurance: if the agent is going to shop, better to be the tool it shops with.

Shopee and Lazada face a sharper version of the same trade. Amazon blocked Muse partly because, it said, an agent shopping for a customer bypasses the personalisation built into its store. For SEA marketplaces whose economics lean on in-app discovery and sponsored listings, handing the browsing to Meta’s agent is no small concession. Shopee has shown a willingness to open its storefront to outside discovery, most recently by teaming up with Pinterest in Indonesia. Muse goes a step further: the customer may never scroll the app at all.

Neither Meta nor its partners have said whether money changes hands, who keeps the customer data, or how the agent decides which marketplace wins a query. Those answers will decide whether this is a partnership or a slow handover of the shop window, and they sit at the heart of the agentic commerce trust gap nobody has yet fixed.

The safety pitch, and the asterisks

Meta says most of its engineering effort went into safety. Each Muse runs on Muse Secure VM, a dedicated cloud computer that stores credentials for connected services while keeping passwords and payment methods out of the agent’s view. Users choose what Muse can touch, and it checks in before sensitive actions such as sending an email or making a purchase.

That is the right architecture on paper. In practice, Meta delayed the US launch to work on security after mixed internal testing, and The Information reported that the agent sent emails without approval during those tests. Amazon also alleged Muse appeared to capture and store customer credentials, which Meta disputes.

Singapore is a demanding test bed. Local firms lead APAC in deploying AI agents, but also in rolling them back, and payments players are already debating “Know Your Agent” checks alongside KYC. An agent that buys the wrong nappies is an annoyance. One that books the wrong non-refundable flight is a customer-service crisis.

A crowded race for your errands

Meta is hardly alone. OpenAI has Operator, Google has rolled out Gemini Spark to work in the background, Perplexity has its Comet shopping agent, and Amazon is building its own buying assistant into Alexa. Muse’s edge is reach: Meta already owns WhatsApp, Instagram and Facebook, and a week after its US debut Muse was the top free app on Apple’s US App Store, ahead of ChatGPT.

Also Read: When AI agents start acting on our behalf, security gets more complicated

Southeast Asia is also where Meta is leaning hardest into chat commerce, having appointed Dhruv Vohra to lead its regional business as AI reshapes online retail. Launching here with signed partners rather than uninvited browsing suggests Meta learned something from its Amazon spat.

“Muse is your personal agent, built to help make everyday life easier,” said Nicole Tan, Managing Director of Meta Singapore.

Whether easier for users also means better for Shopee, Grab and the rest is the question the press release leaves out. For now, Muse has been invited in. Staying welcome is the harder part.

The post Meta’s Muse lands in Singapore with a guest list Amazon never got appeared first on e27.

For years, the anti-phishing playbook has been drilled into office workers like a fire-safety briefing: check the sender, hover before you click, never type your password into a page you reached from an email. The latest data from Check Point Research suggests attackers have read the same briefing. Increasingly, they are not after your password at all. They want you to open a terminal and paste in their code yourself.

The Nasdaq-listed cybersecurity company’s Brand Phishing Ranking for Q3 2026, released on Wednesday, shows Microsoft remained the most impersonated brand, accounting for 33 per cent of all brand phishing attempts tracked in the quarter, up from 23 per cent in Q2. Apple climbed two places to second with 8 per cent, level with Google in third. LinkedIn slipped two places to fourth at 6 per cent, and Amazon rounded out the top five at 4 per cent.

Also Read: Singapore’s cybersecurity paradox: Leading in digital, lagging in defense

Those five names account for more than half of all impersonation attempts, and Microsoft’s share alone is roughly four times that of any other brand. The logic is not hard to follow. A single Microsoft 365 login unlocks email, cloud storage, Teams chats and often the company’s single sign-on, so one stolen credential works as a skeleton key to the whole office.

What the release does not explain is why Microsoft’s share jumped ten percentage points in a single quarter, or how many attempts the ranking is built on. Without that denominator, the percentages are best read as a direction of travel rather than a census.

From stealing passwords to borrowing your hands

The more interesting part of the report sits below the league table. In Q3, Check Point says, attackers followed users into AI and developer tools, and several campaigns stopped bothering with credential theft altogether.

One impersonated Claude, Anthropic’s AI assistant, with a fake payment-failure email claiming a EUR500 (about US$580) charge had been declined. The message warned that API access and Claude seats would be paused unless the recipient updated their payment details, a lure aimed squarely at whoever holds both the company card and the API keys.

Another used a lookalike website for Google Antigravity, Google’s AI-powered coding environment, which displayed what looked like a routine Linux installation command. The command actually decoded a Base64-encoded instruction (Base64 turns text into an unreadable string of characters, which conveniently makes malicious code look like harmless gibberish) that downloaded and ran a shell script from an external server. The result was remote code execution: the attacker could run commands on the victim’s machine at will.

A fake Apple support article coaxed Mac users into running terminal commands with the promise of better performance. And a spoofed Docusign page told visitors a reader update was needed before they could view a document. Clicking Continue downloaded FleetDeck, a legitimate remote monitoring and management tool that IT teams use to administer devices, and which an intruder can use just as easily.

None of these campaigns exploits a software flaw. The victim does the installing. That is the point: security software trained to flag malware has little reason to stop a genuine remote-access tool, and a developer who pastes an install command from a web page is doing something the industry has spent years normalising.

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

“Brand phishing is entering a new phase,” said Omer Dembinsky, Data Research Manager at Check Point Research. Attackers, he added, are “moving beyond password theft to convince victims to run malicious commands or install remote access software themselves.”

Why this lands differently in Southeast Asia

The shift matters for a region whose startups run lean. In a 15-person company in Jakarta or Ho Chi Minh City, there is often no finance team between a billing alert and the person who pays it, and the engineer installing a new AI coding tool may also be the only one who would notice it misbehaving. Speed and informality, the traits founders prize, are exactly what these lures exploit.

Regional data points the same way. A Mimecast study published in July found 65 per cent of IT and security decision-makers surveyed in Singapore and Australia expected an AI-enabled attack on their organisation within 12 months, while 60 per cent said they were not fully prepared for AI-driven threats that exploit human weaknesses. Lean SMEs, often without a dedicated security hire, sit at the soft end of that gap.

Policymakers are busy, but mostly on the consumer front. Singapore has tightened scam rules for messaging, social media and e-commerce platforms, wants Google’s AI to flag scammers before victims transfer money, and its police worked with Meta to disrupt 3.7 million scam-linked assets on Facebook and Instagram, after authorities earlier knocked out 30,000 iMessage accounts tied to scams.

Those efforts target the channels where ordinary people lose their savings. A fake API bill in a developer’s inbox, or a poisoned install command on a cloned product page, falls largely outside them and lands on companies’ own defences instead. Add the rise of deepfake fraud, and the weak point looks less like the software than the moment a busy person decides to trust a familiar logo.

The fix, according to the firm that sells it

Check Point’s prescription is to stop phishing before it reaches inboxes, use AI-powered detection for brand impersonation and credential harvesting, automate investigation and response, monitor for unapproved remote management tools, and scan the wider web for lookalike domains and cloned login pages so they can be taken down. All sensible, and all, unsurprisingly, categories Check Point sells into.

Also Read: Ransomware in Singapore is becoming a human problem, not just a malware one

For smaller teams without an enterprise security budget, some of the most effective controls cost nothing. Billing problems can be handled only by logging into the vendor’s console directly, never through an emailed link. Developer tools can be installed only from documentation reached by typing the address yourself. And a short list of approved remote-access software makes anything else stand out.

Generative AI, Dembinsky warned, is making convincing emails and cloned websites cheap to produce at scale. The uncomfortable corollary is that the next breach at a regional startup may not need to break anything. It will just need someone to copy, paste and press Enter.

The post Phishers now want you to type the malware in yourself appeared first on e27.

Open 10 startup websites. Cover the logos. Shuffle the pages. Nothing breaks. “Revolutionising how finance works.” “Trusted by industry leaders.” Nobody asks which leaders.

The usual diagnosis is lazy copywriting. It lets the founder off. The real cause is worse: she did everything right.

Imagine a Series A software company. Watch one sentence travel through its second year.

The founder writes it in an afternoon: We connect community banks to their old core systems, so they never have to replace them.

Marketing arrives, because the product needs customers: We help financial institutions modernise.

PR arrives, because the company needs news: The fintech infrastructure company reshaping banking.

Investor relations arrives, because the Series B needs a story: A platform for the global financial industry.

An agency polishes the lot: Powering the future of finance.

Every edit was defensible. Every hire was rational. Every version sounded more professional than the last.

Now look at what went missing. Community. Old. Never have to replace them. Those three phrases were the strategy: a narrow customer, an unglamorous problem, a niche larger rivals might ignore. Nobody deleted them on purpose. Each department simply preferred a bigger sentence.

Call it compound dilution. Nobody lies. The judgement leaks anyway.

No one is paid to decide which customers the company will refuse, which markets it will skip, which promises it will not make. That job belongs to the CEO, who is approving budgets and sitting through board meetings.

Also Read: Male founders are losing confidence. Women are doubling down on AI

The company has acquired professional management. It has lost the person who decides what it is not.

And the damage does not stay in the copy. In this imagined firm, sales reads “financial industry” and starts calling insurers. Engineers, being obliging, build what the insurers ask for. The roadmap bends toward the market investor relations described, not the market that pays. Each choice carries a business case. The sum is a company that traded a market it knew how to win for one where it has no particular edge, without a single meeting at which anyone decided to.

The company stops describing its business and starts building the business it has described.

Silicon Valley has circled this problem since Paul Graham published Founder Mode in September 2024. Drawing on Airbnb’s Brian Chesky, he attacked the advice to hire good executives and leave them alone. The argument soon became a quarrel over micromanagement, and both camps missed the odder risk. A company needs no incompetent executive to lose its way. Three excellent ones, each optimising a different version of the firm, will do it.

A tighter finding sits in a 2025 Harvard Business Review study by Scott Cook, Intuit’s co-founder, and Nitin Nohria, a former Harvard Business School dean. They profiled leaders at Amazon, Danaher, RELX and Toyota. The ones who impressed them did not simply make every decision. They designed how work gets done and how decisions get made.

A CEO who delegates the design of judgement has delegated the company.

That does not make micromanagement the answer. A founder who approves every press release becomes the bottleneck, and specialists earn their salaries by challenging her assumptions. But challenging a strategy and quietly replacing it are different acts.

Who decides whether a new customer segment strengthens the business or distracts it? Whether a bigger market is worth a weaker position? Whether the investor narrative matches the product customers actually buy?

These are not communications decisions. They decide where the next dollar goes.

Also Read: Why con artists get the meeting that honest founders can’t

Investors meet the consequences across a table. Sequoia’s pitch guidance opens by asking for the company in one declarative sentence, then moves to the customer’s problem, why now, the competition, the business model. The sentence is the compressed record of choices already made.

A founder who stumbles in the first minute may still have a good company. Brilliant engineers present badly. Smooth talkers build nothing. But when a polished answer collapses at the first question about the customer, a careful investor should wonder who decided what it says.

The temptation is real for Asian start-ups that raise from Singapore, sell to Japanese corporates and recruit distributors in Indonesia within a single quarter. Each audience deserves its own emphasis. None deserves its own company.

The founder’s job is not to write every sentence. It is to make sure each one traces back to a decision she is prepared to defend. Otherwise professionalisation becomes abdication: more people responsible for communicating the strategy, fewer for keeping it intact.

Picture the next fundraising meeting. The marketing deck says the company sells to everyone. The IR deck says the market is enormous. The agency has made both look convincing.

The investor closes the laptop and turns to the founder.

Which one is the company actually building?

—

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 Every department you add makes your startup harder to understand appeared first on e27.

Ask a typical AI assistant a question and it answers. Amit Suxena thinks that is already yesterday’s AI.

“You don’t need AI where I ask a question and get an answer. I think that’s already passed,” says the GVP and Head of NetSuite Asia, who oversees Southeast Asia, the Indian subcontinent and Greater China for Oracle’s cloud ERP business. “I want AI to give me suggestions before I ask.”

His example is deliberately mundane. An AI notices that a distributor’s average inventory cycle is 22 days, works out that a tweak could bring it down to 17, and tells the owner what those five days are worth in cash. The owner, happy with 22 days because customers tolerate 30, would never have thought to ask.

Also Read: Language was never the problem: Inside SEA’s real AI adoption gap

That pitch, proactive and boring in the best sense, sits at the heart of how NetSuite is trying to sell AI to the region’s growing businesses. It also builds on the argument Suxena and NetSuite founder Evan Goldberg made to e27 at SuiteWorld in August: context, not automation, is the real prize.

Thailand, the surprise package

Asked which markets have surprised him, Suxena does not reach for the obvious. Singapore leads, he concedes, largely because labour is expensive and AI lets a small team do more. China has been among the fastest adopters, though he says widespread adoption there has been “up and down”.

The real surprise is Thailand. “Thailand actually stands out as a country where the founders and organisations have been ahead,” he says.

His explanation is supply chains. Thailand hosts a dense cluster of multinationals, many of them Japanese manufacturers, and the SMEs that supply them are pushed to adopt the latest tools to keep up. The country is also courting AI more deliberately, from an OpenAI-backed startup accelerator to a US$80 billion semiconductor push.

Malaysia and Indonesia, he says, still have catching up to do. Indonesia is the bigger prize: a sprawling archipelago of SMEs and mid-market firms operating under different regional rules. Vietnam, meanwhile, still leans heavily on traditional workforces to deliver output. “Big potential,” he says, diplomatically.

Fear, not cost, held SMEs back

So what has slowed the laggards? Suxena’s answer is fear of implementation more than data privacy. The small business owner’s reflex, he says, is: “I have a small business. Can I actually implement AI? It’s too complicated.”

NetSuite’s response is speed. Suxena points to its accelerated implementation programme, which he says can get a company live in a matter of weeks, along with prebuilt industry dashboards and AI that answers questions in plain language. “Suddenly the fear is taken away,” he says.

The deeper problem, he argues, is fragmentation. SMEs buy best-of-breed tools for each job: one for supply chain, another for finance, a third for CRM. Bolt a third-party AI onto that patchwork and it works blind.

He sketches a leather manufacturer shipping to a US customer who expects delivery in 18 days. An AI optimising logistics alone would rush the order. It would not know the customer pays late, or that another buyer who pays reliably deserves priority, even at an extra four per cent discount. “Everything that AI does is out of context,” he says.

It is a self-serving argument for a vendor selling an all-in-one suite. It is also one e27 keeps hearing: Southeast Asia’s SMEs have a record problem, not an AI problem, and even in Singapore, messy data keeps AI pilots stuck.

No one asks AI what business to be in

Practical AI has become every vendor’s favourite phrase, so what have customers asked for that was not practical? Suxena insists the requests have been grounded. “Nobody has asked us what business should I get into, or should I leave this business,” he says.

Instead, they ask about cash flow. One recurring question is what a new market or product line would do to the books. Another is whether a marketing agency’s sales forecast matched reality; NetSuite’s dashboards now compare the prediction with the outcome.

Also Read: Who referees the AI agents? Enterprises aren’t sure, new report shows

Currency volatility is the region’s chronic headache. Suxena is careful not to overclaim. NetSuite is not a currency trading platform, and “it’s impossible for anyone to predict where currencies are going,” he says. “The only certainty is that the uncertainty is there to stay.”

What the software offers instead is historical correlation: what a firm bought from country A, how long it took to arrive, and how currencies moved across markets C and D. The newer twist is that the AI surfaces those patterns before anyone asks.

What ASEAN can borrow from China

Suxena, who stresses he is not a China expert, sees one transferable lesson in how Chinese firms expand. Many run a two-tier ERP model: a heavy enterprise system at headquarters, and a lighter, multi-currency, multi-jurisdiction system such as NetSuite for overseas subsidiaries.

The point, he says, is that Chinese companies were not constrained by their domestic software when they went abroad. For Southeast Asian founders eyeing regional expansion, the lesson is to pick tools for the markets you are entering, not the one you started in.

Compliance and the hallucination problem

India’s DPDP Act, Indonesia’s PDP law and China’s data export rules all restrict where business data can travel. Suxena’s answer on compliance is reassuring in tone but thin on detail. NetSuite has a product team tracking regulatory changes in each country, he says, and customers are “also expected” to check what they are using is compliant.

His stronger card is that NetSuite’s AI is built in and runs on Oracle’s own cloud infrastructure, so analysis stays inside the customer’s context rather than being handed to someone else. Founders connecting external models through NetSuite’s newer AI connectors may want a sharper answer than that, given how Singapore firms fear data sovereignty failures but remain underprepared.

On hallucinations, particularly agents that go rogue with payments, his mantra is human control. Users decide which tasks an agent can execute alone and which need a yes or no.

He offers a telling example. An AI’s idea of a “bad paying customer” may differ from the owner’s, so before automated payment reminders go out, the owner can review the list. “Being in control is the mantra,” he says. That is sensible, but it also places the burden of catching errors back on the customer.

Rivals, and the car with a free engine

NetSuite is hardly alone. Zoho, Kingdee and a crowd of regional accounting and ERP players are adding AI to cheaper suites, while SAP Business One and Microsoft Dynamics 365 Business Central chase the same mid-market. Suxena declined to discuss specific competitors.

Instead, he set out three tests. First, does the AI see the whole business or just one function? Second, does the vendor own its infrastructure as well as its software? Third, how fast can it be deployed? “You want something which works for you today,” he says, “not something which takes a year to implement.”

The infrastructure test is the shakiest. Microsoft, for one, pairs Dynamics with its own Azure cloud, so Oracle is not unique there.

On price, Suxena is emphatic that AI is not a back door to higher licence fees. Everything Goldberg announced in the keynote a day earlier, he says, is included in the existing subscription. He borrows Goldberg’s line: “When you buy a car, you don’t expect to pay separately for the engine.” NetSuite is also offering a startup version that, he says, costs nothing at the outset, betting customers will scale with it, as some have from under US$100,000 in revenue to multi-billion-dollar businesses.

The junior accountant question

Finance back-office work is a major employer in the Philippines and India. What happens to the junior accountant when AI handles reconciliation and exceptions?

Suxena reaches back to NetSuite’s early days as one of the first cloud companies, when customers feared the cloud would hamper them. Those early adopters, he says, are now far ahead and probably did not cut staff. Junior accountants, he argues, will shed repetitive data-entry work and reskill into more proactive roles.

It is the industry’s standard reassurance, and it sidesteps the harder question of who pays for that reskilling. e27 has argued that agentic AI should worry the people it replaces, and the evidence from shared-services hubs is not yet in.

How the boss uses it

Suxena’s own favourite use is telling. He asked NetSuite’s dashboard which country in his region was most productive and profitable, and where he should add headcount. The AI weighed staff numbers, sales, renewals (“extremely important” for a software company), local regulations and more.

Also Read: SMEs adopt AI four times slower than big business, the fix starts with the PC

The answer surprised him. He would not name the country. “I would have never thought that particular country is the one where I should be investing,” he says.

What has he stopped doing? Digging through forecasts line by line. He now simply asks what the six-month forecast looks like and what risks it carries.

For Southeast Asia’s founders, that may be the most practical lesson of all: the value is less in AI that answers, and more in AI that notices what you never thought to ask, provided the data underneath is in order.

Quotes have been lightly edited for clarity and length.

The post Thailand, not just Singapore, is Asia’s quiet AI surprise, says NetSuite’s Amit Suxena appeared first on e27.

Every enterprise software vendor now has an AI agent story. Oracle NetSuite’s version, unveiled at SuiteWorld in Las Vegas on October 26, comes with two twists: its agents are meant to know your industry, and its newest offering is designed to catch tech startups on day one and keep them until IPO.

The company announced SuiteAgents, a set of AI agents built to help organisations work through industry-specific business processes. They sit on NetSuite’s unified data model, and the company says they draw on its 28 years of accumulated industry knowledge to flag issues, recommend next steps and help carry out work inside existing workflows.

Also Read: Three key learnings from SuiteWorld 2025: Why context, not automation, is the real AI prize

“Business leaders manage a lot of moving parts, and the industry signals that matter can easily be missed when teams are focused on day-to-day operations,” said Evan Goldberg, founder and executive vice president of Oracle NetSuite. The agents, he added, are meant to help finance and operations teams “respond faster while retaining oversight, judgment, and accountability.”

The division of labour is fairly clear. Ask Oracle is the conversational layer, where users put questions to the system using industry-specific skills, knowledge and tools. SuiteAgents work in the background, handling ongoing monitoring and multi-step execution, all within NetSuite’s existing governance models.

Six industries, three shipping

NetSuite has lined up agents for six verticals. For software companies, the agent looks for conditions that can hurt revenue retention, margin, financial health and compliance across contract orchestration, revenue compliance, billing and forecasting. The product agent assesses inventory exposure and supply changes, and advises on what they mean for inventory efficiency, supply performance and margin.

The services agent turns statements of work into editable project plans, reviews staffing capacity, flags time-entry exceptions and assesses project health. In construction, the agent converts estimates into job budgets and prepares payment applications based on verified progress, approved changes and retainage terms, the portion of payment withheld until a job is finished.

The healthcare agent combines inventory, service-line, reimbursement and payer-mix data to identify supply and financial risks, while the nonprofit agent focuses on grant-related financial and compliance issues.

The catch is availability. Only the Software, Product and Construction agents are live in NetSuite Next today. The rest are slated for a future release, and the company’s own disclaimer notes that timing is subject to change and that the announcement is not a commitment to deliver any functionality. Buyers in healthcare, services and nonprofits should read the roadmap as intent, not inventory.

Why Goldberg thinks this time is different

At a press conference after his keynote, Goldberg pointed to NetSuite’s AI Connector Service, launched about a year ago, which he said now has around 13,000 companies using it. What NetSuite learnt from watching those customers, he said, has been fed directly into NetSuite Next and convinced the team that this is “a totally different generation” of business software.

Asked about the mixed results enterprises have had with chatbots, Goldberg conceded that a maturing technology will always produce uneven outcomes, and that AI is non-deterministic by nature. But he cited customer examples from his keynote of “companies saving hundreds of thousands of dollars and thousands of hours”.

Also Read: Who referees the AI agents? Enterprises aren’t sure, new report shows

Goldberg, however, declined to forecast how many of those 13,000 AI Connector users will move into NetSuite Next, saying general availability had only just launched. NetSuite is pursuing what it calls an “outside-in” approach, using AI tools outside NetSuite with its data, alongside an “inside-out” approach, working within the platform. Goldberg expects many users to mix the two: data-heavy NetSuite work inside, and tasks pulling from many other sources outside.

The trust problem

Trust came up repeatedly. Goldberg acknowledged that AI models “always answer very confidently,” which is not the same as being right. His answer is context: keeping most of a business’s data in one place and building the collective knowledge of NetSuite experts and its partner channel into the skills that guide the AI. He said he had shown in his keynote that the same question, run through SuiteSkills on three different AI models, produced the same answer.

That framing will resonate in a region where tech leaders are learning to trust AI agents, but not yet with critical systems.

On data, Goldberg was unequivocal: it belongs to the customer. NetSuite only uses high-level, aggregated usage information, such as which questions get asked repeatedly, to improve the service. For firms already anxious about data sovereignty, that is a necessary assurance, if not a complete one. The question of whether the user or the integrator bears responsibility when AI gets something wrong went largely unanswered.

Free, for now: Quickstart goes after startups

The more strategically interesting announcement may be Quickstart. Goldberg was clear that the target is tech startups, which he said are being created in greater numbers than ever because cloud infrastructure and AI have made building companies easier. NetSuite wants to take them “from their original idea to IPO.”

Quickstart offers accounting and subscription capabilities, plus some product features for startups with a physical element. When companies outgrow it, they can switch to NetSuite Next in one click. It runs largely in the background, so founders can get financial information wherever they work. As for the free tier, Goldberg was candid: “It’s not gonna be free forever.”

Also Read: How a 39-store retail chain in Laos moved from Excel to AI agents

Asked whether micro businesses, which a reporter said make up about 97 per cent of Australia’s 2.7 million small businesses, were in scope, Goldberg said NetSuite would start with tech startups and see how it goes. On whether NetSuite might bundle credit lines or working capital, he pointed to existing integrations with partners and said the company is still learning what founders want, and how to monetise it.

This is a notable shift for a product long pitched at the mid-market. Goldberg agreed that, between Quickstart and SuiteExpress, NetSuite expects to become a choice for far more companies than before.

SuiteExpress, and the wait outside the US

SuiteExpress is NetSuite’s answer to the problem every ERP salesperson knows: messy data, poorly mapped processes and weak governance. Goldberg described it as ingesting everything a company currently does, then using AI and NetSuite’s expertise to map it into something more rational, with companies moving from complicated environments to NetSuite in weeks. Anyone who has seen the hidden cost of cheap ERP implementations will want to see that claim tested.

For now, SuiteExpress is available only in the US. Goldberg said NetSuite is moving products outside the US “way faster than ever” and intends to deliver value to all markets “as close to simultaneously as possible,” but offered no firm date. Southeast Asian firms, already weighing a growing field of AI accounting ERP options, will have to wait.

The competitive logic is plain. NetSuite faces SAP and Microsoft Dynamics 365 at the larger end, and Xero and Intuit’s QuickBooks at the small-business end. QuickBooks is the very system, as one reporter noted, that growing companies have traditionally ripped out when they move to NetSuite. Quickstart is an attempt to get there first.

The zero-day close, and other promises

On NetSuite’s vision of an autonomous financial close, Goldberg was careful. He said he is not a CFO, but that it is hard to overestimate what AI may eventually do; the real question is how quickly. His practical point was human: people procrastinate and leave much of the close until the deadline looms. Agents can do that pre-work continuously, at any hour. Before anyone reaches a zero-day close, he said, finance teams would be “really happy with one day, two days rather than 20 days.”

The pace has changed too. NetSuite now ships monthly releases, something it never did before. An “Auto” setting already picks the AI model for a task, and Goldberg said recommending the right model or reasoning level is a direction NetSuite will pursue. Admins can roll new features out to early adopters first, avoiding a “big bang” launch. That kind of control is what regulated sectors such as banking demand before handing agents real work.

Also Read: Parallel Loop nets US$6.6M to sell enterprises the one thing AI models lack: context

Looking two to three years ahead, Goldberg expects more industry solutions. AI writes code fast, he noted, but working out with customers what actually works does not speed up as much. He is also watching time-series and other data-centric models that could improve forecasting. Seeing around the corner, he admitted, is a claim made for years.

That candour is useful. The plan is coherent, but half the agents have not shipped and SuiteExpress has not left the US. As Southeast Asian enterprises push for AI governance before scale, NetSuite will be judged on delivery, not keynotes.

—

(The author is in Las Vegas covering NetSuite SuiteWorld 2026 at the invitation of Oracle.)

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Bukalapak has kept its profit streak alive, though only just. The Indonesian tech firm reported positive adjusted EBITDA of US$55,088 for the third quarter of 2026, a swing from a loss of about US$991,000 a year earlier. Over nine months, adjusted EBITDA reached roughly US$606,000, against a US$2.9 million loss in the same period of 2025. Quarterly revenue held flat at about US$88 million, while nine-month revenue climbed 19 per cent to around US$308 million.

Gaming now does the heavy lifting. Its marketplaces, Itemku and Lapakgaming, generated about US$77 million in quarterly revenue, and nine-month gaming revenue rose 27 per cent on the back of international expansion. That push has a price: selling and marketing spend jumped 45 per cent year on year to about US$3.9 million in the quarter. Mitra Bukalapak, the MSME unit, saw nine-month revenue fall 21 per cent as it moved to a third-party model, though its margins improved. BMoney, the investment arm, grew quarterly revenue 34 per cent.

With about US$925 million in cash and liquid investments, Bukalapak has room to manoeuvre. The harder question is whether a company once pitched as Indonesia’s e-commerce champion can build a durable business on gaming top-ups rather than the marketplace that made its name.

REGIONAL

Son reportedly picks Malaysia for US$50B AI and data centre push: Prime Minister Anwar Ibrahim said SoftBank’s founder chose Sarawak and Peninsular Malaysia for AI and data centre projects. SoftBank has not confirmed the plan, which would test a grid already strained by Johor’s data centre boom.

Ailytics raises US$7.4M to turn idle CCTV into safety AI: Tin Men Capital led the Series A, joined by SG Growth Capital and Japan’s Safie. The Singapore startup grew to 400-plus deployments in 15 countries, though its 73% injury-reduction figure remains customer-reported.

Singapore commits US$112M to cut industrial emissions: EDB and AEDO will fund carbon capture, utilisation and storage research, with up to US$78M for company-led projects under the new DRIVE programme. Industry accounts for nearly two-thirds of Singapore’s emissions.

SGX hunts for new CEO as Loh Boon Chye plans 2027 exit: Loh’s contract runs to mid-July 2027 and no transition date is set; chairman Koh Boon Hwee will help pick a successor. SGX posted record revenue of US$1.17B and US$595M adjusted net profit in FY2026.

Singapore launches US$59M fund to decarbonise power generation: The EMA-AEDO initiative covers hydrogen, solar and biomass, with targets such as hydrogen extraction costs below US$1 per kg. Startups can co-design projects with public researchers, and the state funds up to 75% of qualifying costs.

Korea’s LS Eco Energy puts US$49M into Vietnam ultracapacitor line: The LS Group unit will build medium-sized ultracapacitor cells at LS Cable & System Vietnam by June 2027, funded partly by a US$26M rights offering. The filing names no customers.

Singapore breaks ground on US$49M low-carbon testbed: Amogy, IHI and local startup Metha8 are the Jurong Island facility’s first users. Amogy will pilot ammonia-to-hydrogen cracking at one tonne a day, while IHI tests catalysts that turn CO2 into aviation fuel.

Danantara CIO defends Indonesian corporates amid MSCI scrutiny: Pandu Sjahrir said 134 of 146 large companies listed in Indonesia turned a profit, with first-half earnings up 15%. He urged investors to separate market-governance failings from corporate fundamentals.

Igloo targets five-fold growth for its insurtech software arm: The Singapore firm will use acquisitions to take Igloo Tech Solutions into the Middle East. Its AI claims tool handled 50,000 cases in Q1 with 94% accuracy, and group EBITDA breakeven is targeted for end-2026.

Carro bets Chinese EV influx will feed its used-car business: The Singapore marketplace now sells Dongfeng, Zeekr and Wuling cars. It also rejected 2008 comparisons over its US$352M car-loan securitisation, saying it holds the junior tranche and takes the first loss.

AnyMind opens in Los Angeles to bridge Asian brands and the US: The Singapore-founded, Tokyo-listed firm has helped 15 Asian brands grow in the US over seven months. It will lead with AnyLive, its live-commerce platform, and agentic AI tools.

Data centres could swallow most new power in Johor and Batam: BMI says Johor’s data centres would absorb more than 90% of Peninsular Malaysia’s new generation by 2031. In Batam, DayOne’s campus alone has contracted 450MW, half the island’s installed capacity.

Singapore and Malaysia renew energy pact on cross-border power: EMA and Malaysia’s Energy Commission renewed their 2020 cooperation pact at Singapore International Energy Week, covering electricity trading, renewable energy certificates, solar, storage and interconnection capacity.

INTERVIEWS AND FEATURES

Wealthy founders hedge with gold, not supercars, HSBC finds: Some 61% of entrepreneurs surveyed plan to add gold over 12 months, and half will raise private equity allocations. The barbell strategy keeps personal wealth defensive and confines risk to their own companies.

Thirty Southeast Asian climate startups building beyond the pledges: Seventeen of the 30 are based in Singapore while Vietnam has none, and about half have yet to disclose a round above US$1M. VFlowTech’s US$20.5M raise leads the list.

INTERNATIONAL

Anthropic’s early backers sit on billions ahead of IPO: With Anthropic privately valued at US$965B, Spark Capital’s US$75M stake has grown more than ninetyfold to US$7B, and Iconiq’s US$6B cheque is worth US$17B. The listing could value the lab at up to US$2T.

Instacart explores a sale after talks with DoorDash and Uber: The US$13B grocery delivery firm has hired an investment bank to weigh its options, The Information reported. Uber reportedly walked away, and Bloomberg says talks are early. Shares rose 9.2% on the news.

Zero Industries raises US$3M to beam laser power to satellites: Maezawa Fund led the pre-seed round for the Japan-linked, San Francisco-based startup, which targets a 2030 orbital demonstration. Efficiency is the hurdle: an NTT-Mitsubishi test delivered 152W from a 1kW laser.

Unacademy founders raising US$73.5M fund for deep-tech startups: Roman Saini will run Longshot Capital full-time. The fund has about US$31.5M in commitments, targets a January 2027 first close, and will back seed-stage AI, defence and robotics bets. upGrad acquired Unacademy at a US$206M valuation weeks ago.

Tata Communications to spend US$450M on fibre for India’s AI corridors: The three-phase rollout adds about 10,000 km of fibre, starting with the Mumbai-Pune-Hyderabad-Chennai corridor before reaching Vizag, Bhubaneswar, Jamnagar and GIFT City. It links data centre clusters with cloud firms, global capability centres and banks.

CYBERSECURITY

Decades-old PHP flaws force site owners to patch or go dark: Human researchers in OpenAI and Wiz’s Hack the Planet initiative found the bugs, but attackers raced to exploit them around disclosure. PHP underpins WordPress and Drupal, leaving under-resourced sites most exposed.

Japan’s privacy law trips up founders using GDPR playbooks: APPI leans harder on consent for cross-border transfers but offers a cloud exception GDPR lacks. Japanese buyers expect vendors to arrive with APPI-level contract terms, and those that do shorten their sales cycles.

SEMICONDUCTOR

GlobalFoundries tests an AI-run control room at its Singapore fabs: Built with Siemens, the centre merges five fab control rooms into one and deploys sensor-laden robots. GF gives no downtime, energy or cost targets, and centralisation hands attackers a single high-value target.

Vietnam’s first chip fab likely to miss 2027 target, no ASML deal yet: Military-run Viettel broke ground in January, yet satellite images show no buildings. Sources say months of talks over a DUV lithography machine remain unresolved, and Dutch export-control exposure is unclear. Viettel insists the US$1.3B project is on schedule.

AI

OpenAI cuts frontier model training off from the live internet: The lab will train on cached and simulated web copies after a model exploited a misconfiguration in September to reach the internet. Anthropic began imposing similar limits two weeks earlier.

Apollo economist finds no AI payoff outside Big Tech: Torsten Slok’s look at the S&P 500 minus seven tech giants shows flat profit margins and revenue per worker, with no evidence of productivity gains. He argues business value hinges on judgement, which AI lacks.

inDrive to test Pony.ai robotaxis in Kazakhstan from 2027: Astana trials will start with safety operators, using Pony.ai’s Gen-7 vehicles and Nvidia’s Hyperion platform. Nvidia says the platform could lower robotaxi development costs, vital in emerging markets where rides are already cheap.

Seven in 10 APAC travellers open to AI handling work on holiday: Booking.com’s Travel Predictions 2027 survey of 28,000-plus travellers across 32 markets found 47% would let AI manage emails and admin on holiday. Booking.com and rival Expedia are both adding AI planning tools.

THOUGHT LEADERSHIP

How AI agents found US$570K in lost sales at a Laos retailer: QUEST EDTECH’s CEO rebuilt LDC’s Excel reporting with AI agents for about US$100 a month. The hardest fixes were human, such as agreeing what counts as out-of-stock, since four definitions gave rates from 0.6% to 77.3%.

Japan needs execution, and Southeast Asian startups can supply it: A former FPT Software analyst argues Vietnam–Japan ties are shifting from outsourcing to co-creation, citing FPT’s AI tie-up with Sumitomo and SBI. Pilots must still turn into paying contracts.

Indonesia’s farms are ageing as young people head for cities: Farmer numbers fell 7.5% in a decade to 29.3 million, and only 2.6 million younger farmers use digital tools. The author says the Petani Millennial programme lacks transparency and urges certainty on land and credit.

Two part-timers and an AI orchestrator behind a Dubai summit: SR21’s Alex Lin explains how AI derived the “Breaking Point” theme, briefed 48 senior leaders and fact-checked speakers in real time. Logistics, he argues, was its least significant contribution.

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Thirteen years after venture capitalist Aileen Lee coined the term, the unicorn has stopped being mythical. There are now 1,879 privately held companies valued at US$1 billion or more, according to a new analysis by BestBrokers, which drew on data from Crunchbase, PitchBook and TechCrunch.

The headline number hides a narrower story. Capital in 2026 is crowding into a few sectors (AI, robotics, defence) and a few countries, chiefly the US and China. Southeast Asia, for all the talk of being the next growth frontier, shows up in the report mostly as a footnote.

A caveat before the numbers: BestBrokers is a broker-comparison site, not a research house, and private valuations are soft by nature. Many reflect a single priced round or a secondary sale. Read them as a gauge of investor appetite, not audited worth.

Also Read: One-person company: Why Southeast Asia’s next unicorn may look nothing like Grab

Here are 10 numbers that define this year’s unicorn class.

1. 1,879 unicorns, and more than half are American

The US is home to 971 of the world’s billion-dollar private companies. China follows with 330, the UK with 85 and India with 77. Latin America, often lumped with Southeast Asia as an emerging venture market, counts 38 across six countries, led by Brazil’s QuintoAndar at US$5.1 billion.

2. US$965 billion: Anthropic is now the world’s most valuable unicorn

The crown changed hands after SpaceX’s IPO, which raised more than US$86 billion. Anthropic now sits at the top, with OpenAI close behind at US$852 billion after a US$122 billion round led by Amazon, Nvidia and SoftBank. ByteDance is third at US$550 billion.

Together, those three are worth roughly US$2.37 trillion. Then comes a cliff: Databricks at US$190 billion, Stripe at US$159 billion and Revolut at US$115 billion. For Southeast Asian AI startups building on top of a few foundation models, that concentration is both a supply chain and a dependency, and regulators are starting to take notice.

3. 233 new unicorns in 2026, and 139 of them are in the US

That is close to 60 per cent of the year’s new billion-dollar startups. China added 34, the UK 14, Germany eight, India six and Canada five. The report names no Southeast Asian startup among the 2026 arrivals.

4. One in four new unicorns is an AI company

Of the 233 startups that crossed the line this year, 56 (24 per cent) are in AI. They range from frontier labs such as DeepSeek to sellers of coding agents (Factory), customer-service agents (Wonderful) and video generators (PixVerse).

AI firms also hold seven of the 13 most valuable new unicorns. Hark, founded by Figure AI CEO Brett Adcock, hit US$6 billion after a Series A of more than US$700 million. World-model developer General Intuition is at US$6.2 billion, and the UK’s Ineffable Intelligence reached US$5.1 billion on what the report calls Europe’s largest-ever seed round. Closer to home, governments are trying to engineer the next wave, with Thailand roping in OpenAI for a public-private accelerator.

5. 29 robotics unicorns, and China minted 17 of them

Robotics is the second-busiest sector, accounting for 12.4 per cent of new unicorns as humanoids and embodied AI attract serious money. XPENG Robotics leads China’s cohort at US$6.3 billion, followed by AI2 Robotics and X Square Robot at US$3 billion each. The US has added seven, led by Mind Robotics at US$3.4 billion.

Southeast Asia has a foothold, not a fleet. Singapore’s dConstruct raised a US$125 million Series A earlier this year, but the city-state’s lead in the region is less reassuring than it looks. And as one founder told e27, data, not hardware, is the real bottleneck in humanoids.

6. US$91 billion: DeepSeek is the year’s breakout

The Hangzhou-based lab, founded in 2023 by Liang Wenfeng, reportedly raised around US$15 billion in only its second funding round. Its valuation is more than 14 times that of the next most valuable new unicorn.

DeepSeek earned its spot by making low-cost R1 and V3 models that rattled Western rivals while using far less compute. The size of the gap also says something about the scarcity of credible non-US frontier labs, which lets investors pile into the few that exist.

7. 14 defence unicorns, as geopolitics meets the cap table

Germany’s STARK leads at US$4 billion, helped by a EUR300 million (about US$350 million) contract to supply autonomous strike drones to the German armed forces. Nine of the 14 are American, led by Mach Industries, which more than doubled its valuation to US$3.7 billion in three months after a US$600 million Series C extension.

Also Read: Fintech, DeFi and applied AI define Southeast Asia’s new venture discipline

France’s Harmattan AI became the country’s first defence unicorn after a US$200 million round led by Dassault Aviation. Autonomous weapons are now a venture asset class; the debate over how they should be governed has not kept pace with the funding.

8. 21 new fintech unicorns, as Southeast Asia’s fintech tightens its belt

Fintech made up 9 per cent of new unicorns, with Corgi Insurance leading at US$4 billion. Investors favoured payments, lending, financial infrastructure and AI-assisted financial services. Healthtech was close behind with 17, led by US device maker MiRus at US$4.4 billion after a US$1.5 billion investment from Boston Scientific.

The regional mood is cooler. Fintech funding in Singapore dropped to US$499 million as dealmaking turned selective, and the sector’s next phase is about scaling on the balance sheet rather than on user numbers.

9. 27 unicorns put Singapore eighth in the world

Singapore is the only Southeast Asian country the report breaks out. Its unicorns are led by data centre operator DayOne, which has filed for a US listing. The ranking shows the city-state punching above its weight. It also shows how much of the region’s unicorn story still runs through one small island.

10. Six new Indian unicorns, including a homegrown LLM maker

India has 77 unicorns in total, topped by Walmart-backed PhonePe at US$14.5 billion. Its 2026 class is small but telling: fintech lender KreditBee and AI startup Sarvam AI lead it at US$1.5 billion each. Sarvam’s arrival suggests investors will back a local-language model maker outside the US and China, at a far smaller price tag.

What it means for Southeast Asia

The 2026 unicorn class is built on capital intensity: compute clusters, humanoid hardware, chip fabs and defence contracts. Those are bets that need patient money in very large cheques, and Southeast Asia’s venture market, still recovering from its correction, is not built to write many of them.

Also Read: Echelon Philippines 2025 – The Unicroach vs Unicorn approach: Chasing profitability over growth at all costs

That does not leave the region empty-handed. It leaves it with a choice between owning a layer of the AI and robotics stack or renting one from the companies at the top of this list. The super-app era taught the region how to build distribution; this cycle is asking whether it can build anything that others need to buy.

For now, the unicorns are multiplying. Most of them just aren’t grazing here.

The post Anthropic, DeepSeek and drones: 10 takeaways from the 2026 unicorn boom appeared first on e27.

Everyone is worried about the wrong AI failure.

For two years, the loudest AI safety conversation has centred on hallucination: the model that invents a fact, a court case, a source that never existed. That risk is real and well documented.

But a quieter problem is spreading underneath it, and it may end up mattering more. AI answers can be completely accurate and still be impossible to check. Increasingly, that isn’t an edge case. It is the default experience.

The click is disappearing

Zero-click searches, where a person gets an answer and never visits a website, reached 68 per cent in early 2026, up from 60 per cent in 2024, according to data reported by DesignRush. Global publisher traffic fell 33 per cent year-on-year, small publishers lost 60 per cent of their referral traffic, and Business Insider lost more than half its search traffic and cut 21 per cent of its staff.

This isn’t really a story about journalism nostalgia. It’s a story about incentives. The people who once had a reason to produce timestamped, checkable, primary information — the reviews, price histories, safety recalls, and on-the-ground reporting that both humans and AI models quietly depend on — are losing the traffic that funded doing it properly.

The citation is disappearing faster than the click

Even when an AI system answers using real, current information, it rarely tells you where that information came from. Similarweb data reported by Search Engine Journal found that only 6.8 per cent of ChatGPT answers included an external link as of May 2026, up from roughly 1 per cent a year earlier. That still means 93 out of 100 responses send the user nowhere to verify anything.

Meanwhile, the machines are reading more of the web than ever. Digiday reports that automated traffic now accounts for 57.4 per cent of all web requests, more than actual humans, and AI agent traffic grew 45 per cent quarter-on-quarter in the second quarter of 2026 alone. Yet 97 per cent of the files publishers built specifically so AI systems could read and credit them received zero requests.

Put plainly: the machines are consuming the open web faster than ever, crediting almost none of it, and the people who built that web are being paid for it even less than before.

Also Read: Ecosystem Roundup: Taiwan blocks Grab’s US$600M foodpanda deal over Uber’s stake

This stopped being just a search problem. It’s now a money problem

The same pattern is showing up at the exact moment it matters most: when people decide what to buy. Digital Applied reports that half of US shoppers say they have made a purchase after researching it inside an AI tool, even when the transaction itself happened elsewhere. But in-chat checkout — the one step in that journey that leaves an auditable, timestamped record — converts roughly three times worse than a normal click-through to a retailer’s own site. OpenAI has already partly retreated from it, and Walmart’s Daniel Danker described in-chat checkout as “a very temporary moment in time.”

So the part of the shopping journey moving fastest into AI is discovery and reasoning, the part with no receipt. The part that still leaves a paper trail is the part being pushed back to ordinary websites. That should concern anyone who thinks about consumer protection, not only people who worry about publishing economics.

Why I keep returning to this

Before I built anything in software, I worked as a paramedic. The habit that job drilled into me was never cleverness. It was sequence: check the evidence, understand the situation, then act. You don’t get to skip the first step because the second one feels urgent, and in an emergency, skipping it is how people get hurt.

A great deal of AI product design today runs in the opposite order. The action — the answer, the recommendation, the purchase — arrives instantly. The evidence behind it, if it exists at all, is buried, unlinked, or already gone by the time anyone would think to check it.

Also Read: How AI became my sparring partner

What “showing your work” should actually mean

None of this is an argument for slowing AI down or forcing every chatbot to behave like a search engine with extra steps. It is an argument for a narrower, buildable standard: any AI system that influences a real decision — what to buy, what to believe, what to do next — should be able to show, on request, what claim it made, when it made it, and what that claim was based on.

That is a design requirement, not a moral lecture. Timestamps and traceable sourcing are inexpensive to build in from day one and expensive to bolt on after the fact. Most teams currently treat them as optional, if they think about them at all.

Southeast Asia does not have the luxury of waiting for someone else to solve this first. The region is folding AI assistants into commerce, banking and everyday decisions at speed, often for consumers who have far fewer alternative ways to double-check a claim than someone with a research team or a market data terminal. If the underlying layer of verifiable information keeps thinning out while AI interfaces keep expanding on top of it, the gap between people who can check what they’re told and people who can’t will not close. It will widen quietly, behind a much friendlier-looking interface.

The fix is simple to describe, even if it takes real engineering discipline to build: keep the receipts, and put them where the person making the decision can actually see them.

An answer you cannot check is not intelligence. It is just a well-formatted rumour.

—

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 post The most dangerous thing about AI isn’t hallucination. It’s that it stopped showing its work appeared first on e27.

In a convention centre full of AI agents, autonomous closes and pyrotechnic keynotes, James Chisham has the least glamorous job at Oracle NetSuite’s SuiteWorld in Las Vegas.

He is the man who makes the software obey the taxman.

As Vice President of Product Management and International Strategy, Chisham is responsible for making a US-built ERP behave correctly in Kuala Lumpur, Manila, Mexico City and Munich. In Southeast Asia, that means a patchwork of e-invoicing mandates, tax engines and data rules that change faster than most finance teams can update their spreadsheets.

Also Read: Evan Goldberg on AI trust: Confident answers aren’t enough, context is everything

In a conversation with e27, Chisham was candid about where NetSuite is ahead, where it is still catching up, and where the answers remain, in his own words, “a mix”.

One door, many mandates

Chisham’s starting premise is simple. E-invoicing is coming everywhere.

“There’s every likelihood that every market will eventually adopt e-invoicing, so we wanted to be ahead of that wave,” he said.

Southeast Asia is already proving him right. Malaysia has been phasing in MyInvois, its national e-invoicing system, while Singapore is rolling out InvoiceNow requirements for GST-registered businesses. Each mandate has its own formats, validations and deadlines.

NetSuite’s answer, according to Chisham, is what he calls an “extensible core”. Customers create and receive invoices in NetSuite Next exactly as they always have, while the system handles each jurisdiction’s requirements in the background.

“It could be a Malaysian mandate, a Singapore mandate, a Germany mandate, a France mandate. As far as the NetSuite Next experience is concerned, it works in the same way,” he said.

He argued that this is the company’s edge over specialist e-invoicing providers, the firms for whom compliance is all they do. “Our advantage is that single point of entry, multiple mandate support,” he said. For a growing startup expanding from Singapore into Malaysia and the Philippines, one vendor instead of three has obvious appeal. Whether one vendor can stay as current as three specialists is the question buyers should press on.

Demand first, Vietnam next

NetSuite’s localisation footprint in the region follows its customers, not a map. Chisham said the company has been “driven by where we have our demand, where we have a big customer base”, naming markets such as Singapore and the Philippines.

That explains the gaps. NetSuite’s Southeast Asia localisation package officially covers Malaysia, the Philippines, Singapore and Thailand. That leaves out Indonesia and Vietnam, two of the region’s fastest-growing economies.

Chisham signalled that this will change, at least for one of them. “Markets such as Vietnam are of good interest to us. Vietnam is growing rapidly,” he said.

Also Read: Who referees the AI agents? Enterprises aren’t sure, new report shows

Asked which single APAC market he would prioritise for deeper localisation, though, he looked beyond Southeast Asia. “We’ve definitely got a strong focus on India,” he said, pointing to a large existing customer base there, before adding Vietnam and Thailand as “good markets”. Indonesia did not come up.

Can an agent learn a Malaysian ledger?

SuiteExpress, NetSuite’s new AI-driven implementation tool, promises to move companies onto the platform in weeks. In Southeast Asia, however, the slow part of an ERP rollout is rarely the core setup. It is the local plumbing: statutory configuration, bank payment formats and tax mandates.

Chisham argued that the underlying technology does not change by country. SuiteExpress draws on both a NetSuite knowledge base and the company’s accumulated experience of implementing customers in markets such as Thailand, Malaysia and Vietnam. “The actual technology is the same,” he said. “The AI learns very quickly.”

That is a confident claim. Anyone who has lived through the hidden cost of cheap ERP implementations in the region will want to see it proven on a messy Malaysian subsidiary, not just a tidy US one. SuiteExpress is currently available only in the US.

There was firmer news on a long-standing technical irritant. NetSuite’s own documentation has long warned that the Southeast Asia localisation package does not work alongside SuiteTax, the company’s newer tax engine. Chisham said that work is “largely complete”. The package is becoming hybrid, so it will support both SuiteTax and the legacy tax set-up that, he acknowledged, the majority of customers still use.

“There’s still some work to do,” he said, promising delivery “in due course”. It is good progress, but not yet a ship date.

Who pays when the agent gets it wrong?

With SuiteAgents and SuiteSkills, AI will increasingly create and post transactions on its own. So who carries the liability when an agent issues an invoice that fails MyInvois validation or breaches InvoiceNow rules?

Chisham called it “a pretty good question”. Then, like most of the industry, he answered a slightly different one. Rather than assigning responsibility, NetSuite wants to stop errors before they happen.

“What we want to do is help our customers make sure that their source transactions are correct before they go anywhere else,” he said, describing validation as a strong use case for AI because the rules themselves are deterministic.

The model for this comes from Latin America. In Mexico, where every electronic invoice is validated through the tax authority’s system, NetSuite has shipped capability that monitors transactions and checks them “before they even leave NetSuite”. Chisham said the same approach will be extended across the suite.

Prevention is sensible. It is not the same thing as accountability, a gap enterprises across the region are still struggling to close.

“It is a mix”: where your data goes

Indonesia, Vietnam and India all impose data localisation rules of varying strictness. When an APAC customer asks NetSuite’s AI a question, where is that data processed?

Also Read: How a 39-store retail chain in Laos moved from Excel to AI agents

Chisham’s answer was more honest than tidy. Ask Oracle, NetSuite’s conversational layer, uses elements of OpenAI’s models. “What we do is leverage the OpenAI instance that is closest,” he said.

The closest instance, however, is not necessarily an in-country one. For NetSuite Next, Chisham said the company carries out “a full legal and compliance validation” before moving forward, to make sure it works within the confines of data residency and data processing rules.

He returned to the subject unprompted at the end of the interview, offering to follow up with specifics “because it is a mix” across the different models NetSuite uses. For firms already anxious about data sovereignty, that candour is welcome. A clear, country-by-country answer would be more welcome still.

Human in the loop, all the way down to the SQL

This year’s SuiteWorld theme is the “autonomous suite”. NetSuite’s AI team has also long insisted that keeping a human in the loop is non-negotiable. How do the two fit together?

Chisham’s answer was the most concrete of the interview. NetSuite is building around auditability, and Agent Studio, its agent-building environment, is “built on the core premise” of traceability.

He offered an example. A user asks for the revenue figure for a Malaysian subsidiary. They can then interrogate exactly how the AI arrived at it: the logic the model applied, and even the SQL query and the database tables the number came from. Agent actions are logged the same way, with a full trail and chat history.

If a number looks wrong, the user can step in and tell the model which logic to apply instead. “Whilst we can automate many tasks, we always allow an entry point for the user to make a different decision,” Chisham said.

That level of traceability is exactly what regulated sectors such as banking demand before handing AI agents real work. It also echoes the lesson of last year’s event, that context, not automation, is the real AI prize.

Listening, mostly to the regulator

Asked to name one APAC customer conversation that changed his roadmap, Chisham at first fell back on a familiar line: the roadmap “is really driven by customers”. He then offered a specific example. Direct feedback from customers in Singapore and Malaysia on the e-invoicing user experience led NetSuite to go back and simplify the capability.

Also Read: Banks want AI agents to move money — as long as someone is accountable

His group, he admitted, is driven “heavily by compliance, because there’s so much of it” in Southeast Asia and Latin America. That may be the most revealing line of the conversation. In this region, NetSuite’s product roadmap is written as much by tax authorities as by founders.

For startups weighing their growing list of AI accounting and ERP options, Chisham’s pitch is coherent: one system, many mandates and an audit trail down to the query. The proof will come when SuiteExpress lands outside the US, the SuiteTax fix ships, and someone can explain, country by country, exactly where their data goes.

The post NetSuite’s James Chisham on SEA’s e-invoicing maze, data residency and who pays when AI gets it wrong appeared first on e27.