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.

The post Ecosystem Roundup: Bukalapak stays in the black as gaming carries the load 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.)

The post Evan Goldberg on AI trust: Confident answers aren’t enough, context is everything appeared first on e27.

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.

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.

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.

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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.