One of the biggest changes AI has brought to my work is that I can do more. I am no longer as daunted by the mundane work involved in getting something started.

A research idea, teaching session or presentation comes with preparation: organising notes, building a structure, condensing information and revising the wording. These tasks are necessary, but the effort can become a barrier.

AI makes that preparation feel more manageable.

Across clinical practice, research, teaching and healthcare innovation, I use it to synthesise evidence, challenge ideas, review documents and prepare for discussions.

The benefit goes beyond completing an existing task faster. It changes how willing I am to begin. But being able to produce more makes deciding what deserves my attention more important.

Start with a specific task

Recently, I asked AI to tighten a healthcare innovation presentation to ten minutes. My instruction was simple: less is more.

Producing material was easy. Deciding what deserved the audience’s attention was harder.

The task involved taking material developed for an innovation discussion and reducing it to fit the time available. AI could help reorganise the argument, simplify explanations and suggest shorter headings.

However, shortening a deck requires more than removing words. Which points support the discussion? Which can be dropped? What should the audience understand or decide afterwards?

Those questions provide the standard against which to assess the output.

This is why defining the task matters. “Improve this presentation” leaves considerable room for interpretation. A specific audience, purpose and time limit give the work clearer boundaries.

For founders and operators, the equivalent might be an investor update or management paper. Before generating the document, identify the decision it needs to support.

A small error in this article

Preparing this article provided a concrete example of why checking matters.

My original instructions about the presentation were “Tighten for 10 mins presentation” and “Less is more”.

AI rewrote them as “Tighten for a 10-minute presentation. Less is more.” It improved the grammar but retained quotation marks, presenting edited wording as a direct quotation.

When I subsequently asked for verification and a word count, the review identified the discrepancy. The revised opening paraphrases the request.

The meaning had barely changed. Yet quotation marks make a specific claim: these were the words used.

Also Read: The sovereign threshold: Why airtight AI decisions are paralysing executive teams

When AI transforms source material, it can preserve the general meaning while changing a detail that matters. In research, a more consequential example would be turning “associated with” into “causes”: a small wording change with a substantial change in meaning.

Asking AI to check its work helped surface the quotation issue. However, another AI response is not independent evidence. The original wording made the discrepancy verifiable.

Use summaries to begin the review

Another recent task involved asking AI about the study design and appropriate critical appraisal tool for a national linked-data study of out-of-pocket healthcare costs in Australia.

The question was more specific than “summarise this paper”. I wanted to understand how to assess the evidence.

AI can explain terminology and compare appraisal approaches. Selecting an appropriate tool, however, depends on what the researchers actually did.

Was an intervention evaluated? How were participants selected? How were variables measured? Which biases could affect the findings?

The answers must come from the methods.

The practical lesson is to use the summary as an entry point, then return to the source for claims that determine the appraisal.

The same applies to market reports and customer research. Understanding the conclusion is insufficient if the method cannot support it.

Challenge polished but empty answers

Some output needs correction because it says too little.

I have asked AI to make writing less like a “motherhood statement”: something agreeable that offers limited practical direction.

Consider: “Healthcare organisations should embrace innovation to improve care.”

What should the reader do after reading that sentence? Which problem deserves attention? What would change? How would improvement be assessed?

The sentence is not necessarily false. It is insufficiently specific to guide action.

AI can make this harder to notice because fluent language gives an argument the appearance of completion.

A useful test is whether the paragraph still works after replacing the organisation’s name with another. If it does, it may need more context.

For a founder, “we use AI to improve efficiency” raises similar questions. Which task changes? Who benefits? What happens when the output is wrong?

Also Read: The AI told me to raise my prices by 40 percent but it had never been to a wet market

More capacity still requires judgement

I have not stopped reviewing or editing because AI is available. What has changed is my willingness to tackle work that includes substantial routine preparation.

I can ask it to organise material, propose an outline or shorten a draft, then examine and improve what it produces. The initial work feels less daunting.

I think of this as a cognitive multiplier. It makes it easier to develop ideas and explore alternatives.

However, generating a draft is only part of the workflow. Reviewing, correcting and adapting it also count.

A field experiment involving 758 Boston Consulting Group consultants found that participants using GPT-4 completed tasks within its capabilities 25.1 per cent more quickly on average. On a task outside those capabilities, AI users were 19 percentage points less likely to produce the correct solution.

This was a consulting experiment, not a clinical evaluation. It does not establish how much time AI saves in my work.

It illustrates why benefits should be assessed task by task. A quick draft that needs extensive repair may offer less value than its initial speed suggests.

The relevant questions are whether the finished work is useful and whether it deserved to be done.

Keep ownership of the decision

AI can help develop a coherent research proposal. Whether the study should proceed depends on its importance, feasibility, participant burden and potential to change practice.

Operational decisions require similar judgement.

Consider an appointment no-show prediction system. Identifying someone as likely to miss an appointment does not determine the appropriate response.

A reminder, telephone call and help with rescheduling involve different resources and assumptions. Missed appointments might reflect transport difficulties, caregiving responsibilities or an unsuitable appointment time.

Someone must decide which response addresses the problem, who will deliver it and how its effects will be assessed.

Also Read: The 70/30 rule: Why ASEAN banks still need humans as AI takes over

Clinical decisions involve additional considerations, including the patient’s circumstances, preferences and the consequences of acting or waiting.

AI can organise those considerations. The decision requires someone who understands the context and accepts responsibility for the action.

For founders, this matters whenever AI informs hiring, customer support or resource allocation. A recommendation needs an accountable owner.

Practice makes permanent

I believe professionals should use AI appropriately and often. Familiarity develops through working with it, examining the results and adjusting the approach.

But practice does not automatically make perfect. It can make our habits permanent—including the wrong ones.

Repeatedly accepting plausible answers can establish a habit of uncritical reliance. Repeatedly checking important claims, comparing outputs with sources and correcting errors can reinforce a more disciplined approach.

Frequency alone is insufficient. The quality of the feedback matters.

Start with a bounded task where the output is possible to assess. Define what success looks like. Identify which details require verification. Review where the tool helped and where it introduced additional work.

In healthcare, appropriate use also includes respecting confidentiality and approved arrangements for handling information.

The aim is to become better at judging the output, including recognising when additional expertise is needed.

AI has made me less reluctant to tackle the mundane work. It helps me reach a workable draft, a sharper question or a clearer structure more quickly.

Before using the result, I still need to answer three questions: Is it supported? Does it fit this situation? Can I take responsibility for acting on it?

AI helps me get to those questions faster. I still have to answer them.

—

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 AI can help me think faster, but I still have to decide how I actually use AI in healthcare appeared first on e27.

Yesterday did not look like a market finding its footing. It looked like a sequence of moves designed to catch as many people offside as possible, and the evidence lies in the timing and the numbers.

South Korea’s KOSPI crashed 9 per cent to 4,923.48, and a circuit breaker halted trading. That came one day after the same index fell 5.28 per cent to close at 5,383.80 on Nov 5, when sell-side sidecars froze program trading for five minutes, and foreign investors dumped Samsung Electronics and SK Hynix by 7 per cent to 9 per cent each.

By the time the Asian session opened on Nov 6, the damage had spread beyond Seoul. Bitcoin was down 5.78 per cent to US$76,011.79 and underperformed a broader selloff that had begun on Wall Street the previous day. The 98 per cent correlation with the S&P 500 suggests this wasn’t a crypto story. It was a macro risk-off move, and digital assets offered the highest-beta expression of that fear.

The narrative most desks offer is straightforward enough. On Nov 5 the Nasdaq fell over 5.5 per cent as investors reacted to Democratic control of Congress and the fear that a Blue Wave would bring restrictions on AI data-centre spending. Nvidia dropped 9.3 per cent, and AMD plunged 17.3 per cent, and the rout spilled directly into digital assets. The crypto complex fell 5.29 per cent to US$2.55 trillion.

Derivatives data showed total open interest rose 9.79 per cent to US$382.82 billion, and volume surged 12.72 per cent, suggesting speculative activity had built up ahead of the move. That leverage became fuel. Over US$1.08 billion in crypto positions liquidated in 24 hours, with long positions accounting for US$954 million. Bitcoin alone saw US$281.62 million go into liquidation, with longs making up 90 per cent of that total. The forced closure of those positions added selling pressure and accelerated the drop.

Also Read: Can Bitcoin hold US$80,000 after a US$386M short squeeze and US$1.16B in ETF outflows?

The sequence does not look accidental. The initial move did not simply reward shorts. It also invited late longs into a position that became crowded and fragile. Once the macro trigger arrived, the tape reversed with unusual violence. Big players do not need a conspiracy to achieve this.

They need a crowded book, a visible catalyst, and the willingness to press the advantage when the other side is trapped. The KOSPI sidecar on Nov 5 and the circuit breaker on Nov 6 provided exactly that visible catalyst. Once Seoul halted trading, the risk-off signal became impossible to ignore, and selling pressure fed on itself across Asia and into digital assets.

The mechanics explain why the drop felt sharper than the equity move. Crypto open interest sat at elevated levels heading into the election, and the post-election bounce encouraged more leverage on the long side. When the Nasdaq rout hit, forced selling in derivatives did not merely reflect the equity decline. It amplified it.

Each liquidation pushed the price lower, which triggered more liquidations, which pushed the price lower again. The US$1.08 billion figure is the scoreboard for that feedback loop. The 98 per cent correlation with the S&P 500 is the broader context, but the liquidation cascade is the mechanism that turned a macro headwind into a violent repricing.

Bitcoin’s 7-day RSI fell to 36.43, which is oversold territory. Oversold conditions in a leveraged book can persist longer than anyone expects when the marginal seller is a forced seller rather than a discretionary one. Funding rates at +0.0065 per cent also show that positioning had not yet reset cleanly.

Also Read: US$1B ETF outflows, Democratic sweep highly likely, Iran war: Can Bitcoin hold US$75,600?

The near-term picture now hinges on a narrow set of levels and one external variable. Bitcoin is testing the US$75,000 to US$75,600 support zone, and that zone matters because buyers previously stepped in there. If support holds, consolidation is the most likely outcome, and the oversold RSI could support a minor relief rally toward the US$77,000 pivot.

A decisive daily close below US$75,000 would open the door toward US$73,000 and would confirm that the deleveraging event has not fully run its course. The total crypto market cap sits at US$2.55 trillion, and the next major support below the current swing low is the Fibonacci 78.6 per cent retracement at US$2.61 trillion, followed by the 200-day simple moving average at US$2.48 trillion. That last level separates a painful correction from a structural breakdown.

The pump and dump framing is not the only way to read yesterday, but it is a defensible one. The tape did not simply react to the Nasdaq. It first offered a false sense of direction, then punished the side that believed it. The KOSPI circuit breaker gave the move its alibi, and the liquidation engine gave it its speed.

What matters now is whether the US$75,000 floor holds over the next 24 hours while the equity market decides whether the AI trade is merely correcting or genuinely unwinding. If the tech rout stabilises, digital assets can consolidate and rebuild. If the KOSPI panic spreads further into U.S. futures, forced selling will find another leg lower. The big players who wanted quick wins have already booked them. The question is whether the tape can find a bid without them.

—

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 Bitcoin pump and dump? The US$75,000 support zone will decide appeared first on e27.

Indonesia’s anti-corruption framework, codified in UU 31/1999 jo UU 20/2001, allows prosecutors to charge state-owned enterprise officials with corruption when their decisions are deemed to have caused state losses (kerugian negara), even in the absence of personal benefit, kickback, conflict of interest, or bad faith. The policy debate around this framework has been active for two decades. The question of how Indonesian law should treat investment risk inside state-owned enterprises has not been settled.

After 15 years inside Indonesian risk functions, I have watched this debate from the operational side. The gap between business judgment and criminal exposure is not theoretical. It is something senior managers in BUMN institutions operate inside every quarter.

The legal framework that creates the gap

Indonesian anti-corruption law contains three elements that, together, produce the structural exposure.

The kerugian negara element. The statute allows prosecution when an act has caused harm to state finances. Constitutional Court ruling MK 25/PUU-XIV/2016 established that the loss must be actual and concrete, not merely potential. The threshold for what counts as actual loss has been the subject of substantial litigation since.

The mens rea question. The law does not always require proof of personal benefit or bad faith. An official can be prosecuted for a decision deemed contrary to law (melawan hukum) that produces state losses, even where the decision was made through formal institutional processes.

The absence of a business judgment doctrine. Common-law jurisdictions have evolved business judgment rules that protect corporate officers from second-guessing of decisions made in good faith with reasonable information. Indonesian commercial law does not have a directly equivalent doctrine for BUMN officials. Investment decisions that look like prudent risk-taking under one framework can look like criminal exposure under another.

The risk management implications

Three operational consequences follow.

Documentation as primary defence. The most important practice for BUMN investment officials becomes the quality of decision documentation. Every alternative considered, every external review, every committee approval, every dissenting view recorded: these become the evidentiary record years later. The cost of weak documentation is paid in a courtroom.

Also Read: Indonesia urgently needs an allergy data centre to support national food security

Conservative bias in investment selection. The framework produces structural preference for conservative, well-documented investments over high-conviction contrarian bets. This is rational individually. It is a constraint on returns institutionally.

Selection effect on senior talent. The most experienced investment professionals have alternatives. Several choose independent funds or private sector roles partly because personal exposure to criminal proceedings in BUMN roles is meaningfully larger than in equivalent private positions.

What healthy practice looks like

Three practices distinguish BUMN investment functions that operate well inside the existing framework.

Pre-decision risk review documentation. The strongest committees document the decision, its justification, alternatives considered, risks identified, mitigation plans, and dissenting views, at the time the decision is made, not after the fact. The contemporaneous record is the asset that protects officials and institutions if decisions are later questioned.

External validation embedded in process. Investments that include genuine external review (independent advisors, third-party valuations, regulatory consultations) produce a decision record that is harder to second-guess. The cost of external validation is meaningfully less than having a decision questioned without it.

Clear separation of business judgment from procedural compliance. Governance processes that distinguish business judgment (was this a reasonable investment?) from procedural compliance (was this approved correctly?) protect against later confusion. Procedural compliance is binary. Business judgment is probabilistic. The two should not be conflated.

Lessons learned

Five principles emerge from observing BUMN investment governance inside the existing framework.

Documentation is the primary risk management tool. Not models, not committees, not advisors: documentation. The decision record is what is examined years later. Institutions that treat documentation as first-order discipline absorb less exposure.

Process is necessary but not sufficient. Following formal approval process is the baseline. It does not, by itself, protect against later criminal exposure if the underlying decision is later deemed contrary to law. Substantive quality matters as much as procedural correctness.

Conflict of interest disclosure is non-negotiable. The cleanest defensible decisions are those where every potential conflict, direct or indirect, present or historical, has been documented and recused. Disclosure cost is small. Non-disclosure cost can be career-ending.

Also Read: The sharia trust premium: Lessons from Indonesia’s first sharia P2P generation

External counsel involvement is insurance, not cost. Major BUMN investments that weathered legal scrutiny best were those where external counsel was involved in the decision pathway, not retained after the fact. Investment in external counsel during the decision process is risk insurance.

The framework itself is a policy question, not a personal one. The structural tension between Indonesian anti-corruption law and BUMN investment risk-taking is a legitimate policy debate. Individual officials, however, have to operate within the law as it currently is, not as they might prefer it to be.

The macro stakes

Indonesian BUMN investment activity matters for the broader economy. State-owned enterprises remain among the largest capital allocators in the country, their investment decisions shape entire sectors, and the talent willing to lead those decisions determines the quality of Indonesia’s strategic capital deployment.

The legal framework around BUMN investment risk will continue to evolve. The risk management practices inside BUMN institutions, however, can evolve faster. The institutions that build documentation discipline, external validation processes, and decision pathways that work inside the current framework will produce both better investment outcomes and lower exposure for the officials making decisions.

The business judgment gap will not close quickly. The institutions that build for it deliberately will operate inside it better than those that hope it will close.

—

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 The business judgment gap: Why BUMN investment decisions need a different risk framework in Indonesia appeared first on e27.

Southeast Asia’s tech startups drew about US$16.55 billion between January and October 2026, according to Tracxn. October alone delivered US$3.68 billion across 14 rounds, 933.7% above October 2025.

The headline flatters the market. January, June, July and October account for 81% of the year’s money, while the other six months averaged about US$520 million each. Eleven rounds across ten companies, led by DayOne, Kling AI, Ant International and Manus, make up roughly two-thirds of the total. Consumer internet, the engine of the last boom, barely features. Several of those giants are Southeast Asian mainly by address: Kling AI, Ant International and Manus, all Chinese in origin and now headquartered in Singapore, contributed about US$4.5 billion between them.

Deal volume tells the quieter story. Tracxn logged between 11 and 23 rounds a month, averaging just under 15, and most were seed or early stage. Series B and C rounds remain scarce, while state-linked investors such as EDBI, SEEDS Capital and ADB Ventures keep surfacing on the most-active lists.

Tracxn’s revisions add another caveat: September’s US$81 million has since become US$209 million. For founders, the number that matters is the flat 15 rounds a month, not the billions.

---

REGIONAL

Google commits US$15B to Malaysia, pledges to pay its own power bill: The 2027–28 package, unveiled with Prime Minister Anwar Ibrahim present, bundles new data centre campuses with skilling, water and clean energy pledges. It dwarfs the US$2 billion Google committed in 2024 for its first Malaysian data centre.

Microsoft picks Johor Bahru for its second Malaysian cloud region: Southeast Asia 3 forms part of Microsoft’s US$2.2B four-year Malaysia plan and follows the Malaysia West region opened in May 2025. Its backup generators will run on renewable biofuel, and a programme will train 150 graduates for data centre jobs.

Grab posts record US$237M EBITDA, but lending now drives its growth: Net profit reached only US$18 million. Financial services revenue jumped 124% to US$201 million as the gross loan portfolio tripled to US$2.7 billion, helped by the Superbank and Stash acquisitions, with the Atome deal still to come.

Malaysia carried SEA’s IPO market in 2026 while the rest went quiet: EY data shows Southeast Asia logged 56 IPOs raising US$3B in the first nine months of 2026, down from 76 a year earlier. Strip out Malaysia, and the region’s public markets look like a waiting room.

Singapore hands IPO prospectus reviews to SGX RegCo from January 2027: Issuers will file prospectuses and listing applications through one SGX RegCo electronic portal, replacing separate MAS reviews. SGX RegCo targets six to eight weeks per complete application and will stand down its Listings Advisory Committee.

Visa opens six global capability hubs in Singapore with 1,500 staff: Based at a new Paya Lebar Green Solution Center, the hubs will work on agentic commerce, stablecoins and quantum security with partners including DBS, Nium and StraitsX, and join MAS’s BLOOM cross-border digital money initiative.

Pine Labs and Stripe unify online and in-store payments in Singapore: From November, eligible retailers get one view of all transactions across Stripe’s online rails and Pine Labs’ terminals, plus instalments, pay-by-points and promotions. The partners plan to expand to other Southeast Asian markets in 2027.

Malaysia launches Startup ASEAN digital centre at Cradle LIVE! 2026: The Digital Center of Excellence adds seven modules, including AI matching, to the Startup ASEAN platform, which already counts more than 52,000 active users and 10,400 startups from over 150 countries.

INTERVIEWS & FEATURES

Myanmar refugees are building Thailand’s AI data centres: A Nikkei Asia photograph of refugees working on a data centre site in Chonburi exposes the labour shortage beneath Thailand’s AI ambitions, and asks who actually benefits from the region’s infrastructure boom.

Taiwan’s envoy sees Singapore as first stop for its global startups: Dr Tung Chen-yuan, Taipei’s representative in Singapore, visited 33 Taiwanese startups at SWITCH 2026 and argues they are capable enough to go global, with the city-state as the natural launchpad.

Afore backed an SUTD founder, then let him scrap his plan in a week: Mohammed Khambhati learnt in his dorm that the San Francisco pre-seed firm was funding him. After flying to SF, he abandoned the original plan within days, and his investors did not flinch.

How DVI Group merged eight systems after its pandemic boom: The Singapore-based audio-visual integrator found its sales, finance, delivery and service teams tracking live contracts with different numbers. General Manager Tanish Dhillon explains how consolidating its systems gave management one figure to trust.

Multiable HCM pitches AI HRIS to Singapore’s listed companies: The piece argues SGX-listed groups face intense scrutiny over workforce governance and disclosure accuracy, and positions Multiable’s AI-driven HR platform for enterprises managing thousands of employees across business units.

INTERNATIONAL

OpenAI could be IPO-ready in 2027, says Greg Brockman: Speaking at a WSJ conference, the OpenAI president said enterprise sales now drive most of the company’s revenue, as it pushes its Codex agent to businesses and steps up fundraising ahead of a listing.

China blocks SoftBank’s US$5.4B buy of ABB’s robotics arm: ABB and SoftBank remain in talks after an NDRC-led security review halted the transaction. The review can reach offshore deals, and China generates about 30% of ABB’s robotics revenue. ABB had earlier planned an IPO spin-off.

Sequoia shuts London office, scraps Arc accelerator and Scouts: The firm will merge its early-stage and growth teams, flatten its partnership, now run by seven general partners, and drop Monday partner meetings. It has raised about US$10B in new funds and will back European founders from the Bay Area.

TikTok quietly launches a standalone TikTok Shop app: The app offers personalised product recommendations alongside livestream and short-video shopping, splitting commerce from the main feed. It is not yet live in the US; Southeast Asia remains one of TikTok Shop’s core markets.

Hong Kong startup funding drops 77% to US$94.1M in October: Tracxn logged just five rounds in October, with Fenorri and PhotonPay taking about 86% of the capital. September’s US$300 million Qupital raise inflated the comparison, and the city recorded no IPOs or acquisitions.

Meta, Google and TikTok sue California over teen social media law: AB 1709 would bar autoplay and personalised feeds for under-16s from January, with fines of up to US$50,000 per affected user. The platforms cite the First Amendment; privacy advocates warn of intrusive age checks.

OpenAI is already trying to shape the 2028 US election: Chief global affairs officer Chris Lehane’s memo calls AI the number-one issue of 2028 and urges candidates to offer a ‘third path’, warning that opposing data centre construction could cost them the debate.

CYBERSECURITY

How a fake job interview opened Binance to North Korean hackers: Chainalysis has tied more than US$14.3M in losses over 18 months to DPRK-linked fake job interviews, the playbook behind the US$6.4 billion Binance theft. Startups hiring remote engineers sit squarely in the target zone.

Binance confirms fake recruiters let North Korea in for US$6.3B heist: Hackers posing as recruiters targeted a Binance wallet software engineer and planted malware on their laptop. Researchers have warned for years that DPRK operatives pose as recruiters, investors and VCs to breach crypto firms.

Every AI budget needs a matching security budget: Writing after the biggest theft in crypto history, JitSiong Thaddeus Koh argues the rush to deploy AI agents and assistants is widening attack surfaces, and companies should match AI spending with equal security investment.

SEMICONDUCTOR

Ondine Capital closes Fund III’s first round with Taiwan chipmakers: The Taiwan-based VC did not disclose the size but will back foundation models, next-generation semiconductors and physical AI. Early bets include silicon-photonics firm Salience Labs, and Brian Yen joins as partner from Headline Asia.

Singapore’s factories ride the AI wave, but on a narrow base: A net weighted balance of 24% of manufacturers expect better conditions between October 2026 and March 2027, the EDB said, but the optimism leans heavily on demand from AI data centres.

AI

Frontier AI money is getting picky. SEA founders should price it in: OpenAI cut its fundraising target by US$10 billion to US$20 billion as tech stocks fell, ending an era in which the biggest ask won. Regional AI startups should expect sharper scrutiny of their numbers.

Singapore tops Commonwealth AI index; Australia shows the real gap: The Ayre Index, launched at CHOGM, ranks young Singaporeans the best prepared for AI. Third-placed Australia offers the sharper lesson for Southeast Asia: having the technology is the easy part.

Nine in ten Singaporeans use AI monthly; only a third feel comfortable: We Are Social and Meltwater’s Digital 2027: Singapore report finds a wide gap between habit and confidence, a warning for AI builders who assume adoption equals trust.

AI chatbots cave on climate facts, and SEA languages may fare worst: A UNESCO and University College London study found chatbots that answer correctly on human-caused warming often start agreeing with users after a turn or two of pushback, a weakness likely worse in Southeast Asian languages.

Anthropic’s compute bill climbs to 58% of revenue ahead of its IPO: Anthropic’s S-1 filing shows compute margins worsening when they should improve, with costs up from 48% of revenue the previous quarter. The company is shifting more workloads to its own infrastructure to cut the bill.

Meta warns election backlash against data centres could slow AI: President Dina Powell McCormick said Democratic midterm wins will make data centres harder to build. Meta now pays for its own electricity and keeps jobs local to win approval, a model Google echoed in Malaysia.

Airbnb to launch its own AI travel agent in the first half of 2027: Rather than a text chatbot, the agent will compose maps, photos and itineraries in real time and handle rebookings and refunds. Airbnb announced it alongside Q3 revenue growth of 19% to US$4.87 billion.

THOUGHT LEADERSHIP

Wall Street’s tokenisation push holds lessons for SEA founders: After five weeks in New York, including Avalanche Summit and UN Blockchain Week, Astrid Dang argues founders should start with the problem first, not the token, as blockchain conversations move beyond crypto trading.

Information asymmetry didn’t die in 2026; it just learned better PR: Faheem Aizat Kamsan contrasts a fintech report declaring information asymmetry dead with Singapore’s largest bank, which is still teaching its AI assistant card refunds, arguing agentic AI hype outpaces banking reality.

The step zero problem holding back SEA’s startup ecosystem: Benedict Kok notes the region’s startup ecosystem was valued at US$454 billion in the first half of 2024, yet argues its increasingly sophisticated support infrastructure still leaves gaps at the very first step.

Global expansion now demands a different mindset: Helmi Talib argues the old cross-border expansion playbook, in which more borders meant more success, no longer works, and companies must now choose where and how they expand far more selectively.

Could AI agents break fractional-reserve banking?: John Millar asks what happens to banks when AI agents can move deposits at short notice all at once, undermining the assumption that customers never ask for their money back simultaneously.

ETF outflows and macro risk test Bitcoin’s US$75,600 support: Bitcoin fell 1.05% to US$75,830.54 in 24 hours. Anndy Lian argues retreating institutional demand, an 85% correlation with equities and geopolitical risk are reinforcing each other.

Startups should plan for scaling, not mere survival: Jon Low argues short-sighted choices on partners, risk and hiring create disruption and burned money, and that founders who get these early decisions right are the ones who scale quickly.

AI might be the final boss for your best ideas: Januar Parlindungan fed his company’s 2026 plan, spanning payroll, billing, cross-border payments and licences, into AI to decide which initiatives mattered most, and reflects on what the exercise revealed.

Using AI for sales outreach without outsourcing judgement: Mustafa Rasheed uses AI not to blast generic emails but to find businesses showing public signals that match his offer, keeping the decision on whom to contact firmly human.

AI speeds up healthcare thinking, but humans still decide its use: Yee Ang says AI has removed the dread of starting research, teaching and presentations, but argues practitioners must still choose deliberately where the tools belong in their work.

I automated my life with AI agents, then a 2 am voice note hit: Vincent Tan spent months building an autonomous productivity engine to remove every bottleneck, until a founder in crisis showed him that execution is cheap, presence priceless.

Why shoppers are tuning out brand messages, and how to stop it: Petros Romas warns that customer disengagement from brand messaging does more damage than algorithm shifts, outages or privacy rules, and often goes unnoticed on dashboards until retailers have lost their customer base.

The post Ecosystem Roundup: SEA startups raised US$16.5B in 2026, but four months did most of it appeared first on e27.

Malaysia’s second 5G operator wants startups to do something telcos across the region have struggled to make happen: build for its network before anyone has agreed to pay for the result.

U Mobile said on Friday that its Enterprise Innovation Platform (EIP) is deepening its collaboration with Cradle Fund, the government-linked startup agency, to help Malaysian startups develop and validate 5G-Advanced (5G-A) and AI solutions for real business use.

The split of duties is simple. Cradle brings the startup pipeline, its programmes and possible funding routes. U Mobile brings a 5G-A and AI sandbox, its engineers and introductions to enterprise buyers.

Also Read: Malaysia’s digital economy’s second wave looks nothing like the first

For the uninitiated, 5G-A is the next layer of 5G standards, promising lower latency, faster uplink and more precise device positioning. That matters far more to a factory robot or a fleet of connected trucks than to someone streaming football on their phone.

The fine print deserves a second read. U Mobile said access to specific sandbox capabilities depends on engagement and technical requirements, and that funding and commercial opportunities are not guaranteed.

In plain terms, this is a test bench and a networking room, not an investment vehicle.

A fast network still hunting for paying use cases

U Mobile has a point to prove. It was named the winner of Malaysia’s second 5G network bid on 1 November 2024 and received its letter of award on 31 March 2025, setting it up as the challenger to state-backed Digital Nasional Berhad. By the end of September 2026, it said its network covered 90 per cent of populated areas, up from 80 per cent in March. It also cites recognition from Ookla as Malaysia’s fastest 5G network, a joint win with Opensignal for reliability, and a Malaysia Book of Records entry for the fastest 5G rollout.

Coverage, however, is the easy part to announce. Monetisation is harder. Operators across Asia have spent heavily on 5G only to find that consumers will not pay much more for faster phones. The real money, if it arrives, sits in enterprise: private networks, edge computing and industrial IoT. Those markets need applications, and applications need developers who understand the network. That is where startups come in.

“Malaysia has no shortage of bold ideas and talented innovators. Startups need the right environment to test their solutions and understand how they can address real business needs,” said Woon Ooi Yuen, Chief Technology Officer of U Mobile.

He added that the Cradle partnership would connect more innovators with EIP’s 5G-A and AI capabilities, technical expertise and industry engagement, helping them “demonstrate their potential to businesses”.

Cradle wants startups with passports, not just pitch decks

For Cradle, the deal is another corporate bridge. Norman Matthieu Vanhaecke, its Group CEO, said the agency’s focus is on building an ecosystem that lets Malaysian companies grow beyond domestic markets. “Ultimately, we want to see more Malaysian startups develop the scale and competitiveness to succeed regionally and globally,” he said.

Partnerships have become Cradle’s default mode. It previously teamed up with Invest India to link the two countries’ startup ecosystems, and this tie-up has been running since September 2026 through the Cradle Roadshow in Sarawak, the MYStartup Pre-Accelerator Demo Day hosted at the EIP Hub on 28 October, and Cradle LIVE! Summit 2026.

Also Read: Google’s US$15B Malaysia bet comes with a promise to pay its own power bill

The Sarawak leg is a quietly useful detail. Much of Malaysia’s startup support still orbits the Klang Valley, and East Malaysian founders working on agriculture, energy or logistics are precisely the ones who could use a connected-industry testbed.

Policy tailwinds, with a funding caveat

The timing suits both sides. Budget 2027 hands startups US$110.4 million, while the government’s AI Nation 2030 plan is pushing B2B adoption. Yet investors have warned that Malaysia’s startup money has a matching problem, with public capital struggling to find enough private co-investment.

That is why a sandbox has real value for early-stage founders. Buying test equipment, edge compute and network access to prove a 5G-A use case is expensive, and a free bench can save months of runway. For U Mobile, every startup that builds on its stack is a potential channel into an enterprise contract it would otherwise have to win alone.

Rivals are writing bigger cheques

U Mobile is hardly alone in courting founders. Singtel launched a US$250 million AI fund to turn its regional footprint into an AI deployment platform, while the Philippines’ Globe has pitched its ecosystem as a fast track to market entryfor startups. At home, CelcomDigi and Maxis chase the same enterprise customers with deeper subscriber bases.

The contrast is stark. Singtel offers capital; U Mobile, for now, offers infrastructure and introductions. Meanwhile, telcos everywhere are racing to put AI into their own apps, which raises a fair question for founders: will the operator be a customer, a partner or eventually a competitor?

There is a local opportunity, though. As Malaysia leans into sovereign AI and local context as a startup moat, solutions validated on a domestic network with domestic enterprise data could carry an edge that imported tools lack.

Demo days are cheap; purchase orders are not

The scorecard that matters will not be the number of demo days or summit panels. It will be how many sandbox pilots turn into paid enterprise contracts, and how quickly.

Also Read: Malaysia carried Southeast Asia’s IPO market in 2026. The rest of the region went quiet

Neither party has disclosed how many startups are in the pipeline, what share of the sandbox they can use, or how intellectual property developed on EIP is treated. Those details will tell founders whether this is a genuine launchpad or a showroom for U Mobile’s network.

Until then, Malaysian startups get a fast network to play with. Whether anyone pays them to keep playing is the real test.

The post U Mobile opens its 5G-Advanced sandbox to Cradle’s startups, but the cheques are not part of the deal appeared first on e27.

Nine in ten Singapore internet users now reach for an AI tool every month. Barely a third say they are comfortable doing so. That gap between habit and confidence is the most telling finding in Digital 2027: Singapore, the annual report released today by creative agency We Are Social and media intelligence firm Meltwater.

According to the report, 89.9 per cent of Singapore internet users aged 16 and above used an AI tool in the past month, ahead of the global figure of 85.2 per cent. Yet only 34.6 per cent say they feel comfortable using such tools.

Put plainly, more than half of the city-state’s online population is leaning on a technology it does not fully trust.

Also Read: AI saves me hours, but I don’t trust it blindly

That is less contradictory than it sounds. Singaporeans have spent years living with search engines that serve ads and social feeds that serve outrage. Using a tool while keeping it at arm’s length is a familiar digital posture. Still, a gap of roughly 55 percentage points is wide enough to matter for the AI firms, brands and startups that treat Singapore as Southeast Asia’s test market.

ChatGPT leads, but search refuses to die

ChatGPT is the clear front-runner. Nearly half (48.7 per cent) of Singapore internet users used it in the past month, well above the 34.7 per cent worldwide. Google Gemini trails at 29.8 per cent.

The obituaries for search, however, look premature. Some 88 per cent of users still turn to a traditional search engine monthly, within two percentage points of AI tool usage. Even regulators are struggling with where one ends and the other begins; the EU has called ChatGPT a search engine, a label with knock-on effects for AI startups in the region.

Among active AI users in Singapore, finding information is the top reason for use (63.4 per cent), followed by getting advice on problems (39.6 per cent) and saving time or boosting efficiency (36.1 per cent). In other words, most people are using chatbots as a faster search box, the very job search engines have done for two decades.

Heavy users, light spenders

The monetisation picture is less flattering. Just 8.1 per cent of Singapore internet users pay for an AI subscription, below the global average of 9.5 per cent. For one of Asia’s wealthiest markets, and one that outpaces the world on usage, that is a notable shortfall.

The release does not explain why, nor does it break the figure down by age or income. But the pattern fits a wider story: free tiers are generous, employers increasingly supply the tools, and Gen Z workers are bringing their own AI to the office regardless of company policy. Why pay at home for something you use at work and only half trust?

For model makers pouring money into Asia Pacific expansion, that is an awkward data point. High engagement with little willingness to pay is the same trap that caught a generation of Southeast Asian consumer internet startups.

Real or fake? Singapore isn’t sure

The trust deficit stretches well beyond AI. Nearly three in four (73.4 per cent) online news consumers in Singapore worry about what is real and what is fake on the internet, the eighth-highest level in the report.

Also Read: The consumer is still human. The gatekeeper isn’t

That anxiety is not paranoia. Deepfake fraud losses have climbed into the billions globally, and Singapore has leaned hard on platforms, including enlisting Google’s AI to flag scams before victims hit send. The chatbots carry their own credibility baggage, with recent research showing AI models can cave on facts under pressure, and Southeast Asian languages may fare worst.

Singapore ranks ninth globally for AI comfort. That sounds respectable until you remember what ninth place is worth here: barely a third of users. If that is a top-ten score, public confidence in AI is thin almost everywhere.

Brands still get found the old-fashioned way

For marketers, the report punctures some of the hype around AI-led discovery. Only 23 per cent of Singapore internet users use AI tools as a main source when researching brands, and just 12.6 per cent discover new brands through them. Search engines (51 per cent), product and brand websites (37.4 per cent) and social networks (34.9 per cent) still dominate.

None of this makes optimising for AI answers a fad. Southeast Asia’s sellers now have to learn to sell to AI, and those discovery numbers will climb. But today, a brand that neglects its search ranking to chase chatbot mentions is optimising for the minority.

Social media remains the main stage. 94 per cent of Singapore’s population has an active social media user identity, and internet users aged 16 and above juggle an average of 7.3 platforms a month. WhatsApp reaches 84.8 per cent, while Instagram commands the most time among the major apps at one hour 45 minutes a day. Singapore also ranks fourth for LinkedIn use and joint sixth for Reddit, and more than a third of social users say reading news is a main reason they log on.

Read the sales pitch between the numbers

The report’s authors have drawn their own lessons. Mimrah Mahmood, APAC Vice President of Enterprise Sales at Meltwater, said brands must understand how they show up “from search and social to AI answers”. Arthur Altounian, Managing Director of We Are Social Singapore, put it more pointedly: “Singaporeans have embraced AI, but they haven’t handed it their trust.” The brands that grow in 2027, he argued, “will be the most believed”, not the most visible.

Also Read: Inside Singapore’s AI push: Lighthouses, rice importers and robot labs

Both conclusions line up neatly with what each firm sells: cross-channel monitoring in Meltwater’s case, creator-led campaigns in We Are Social’s. That does not make them wrong, since reach and credibility are not the same thing in the creator economy. But readers should note who is drawing the conclusions, and that the release discloses neither sample sizes nor survey methodology.

The bigger question is whether trust ever catches up with usage, or whether Singaporeans settle into a permanent state of wary dependence. For AI companies, the answer will decide whether 89.9 per cent usage turns into revenue. For everyone else, the 73.4 per cent who worry about telling real from fake may be the number that matters most.

The post Nine in ten Singaporeans use AI. Only a third are comfortable with it appeared first on e27.

The last few weeks have had a pattern. Every week, another company announces an AI agent, an AI assistant or an AI workflow. Every week, another headline tells us something has been breached, leaked or drained. Those two streams of news are connected.

On 30 October, Binance suffered the biggest theft in crypto history, with US$6.4 billion gone, and Chainalysis’s reconstruction says the outcome was decided in 54 minutes. Binance is a well-funded exchange with a large security team, so this was not a case of a small shop forgetting to lock the door. The speed is the lesson. Attackers no longer move at human pace, and many of them now use agents to do it.

That matters well beyond crypto. A recent study found that AI agents broke into 100 firms for pocket change, and that Southeast Asia’s small online shops fit the target profile. For years, obscurity protected smaller businesses, because breaking in took skill and patience. Agents have made it cheap, and cheap attacks get aimed at everyone.

Also Read: The hidden risk in AI adoption: Unchecked agent privileges

The background rate is climbing as well. Global organisations faced an average of 2,336 cyber attacks per week in July, up 16 per cent from a year earlier, with education, energy and travel among the sectors under growing pressure. Supply chains add to the exposure: in Southeast Asia, supply chain attacks are becoming routine, in a region short of security talent and heavily dependent on third-party technology providers. Add agents that can probe thousands of targets cheaply, and the economics tilt further toward the attacker.

We are building faster than we are securing

The picture inside companies is not reassuring. Gravitee’s research found that most organisations have deployed AI agents into production, yet fewer than one in five have full security approval in place. Adobe’s Southeast Asia findings point the other way, with 57 per cent of firms stuck in AI’s “frozen middle”, unable to get projects past their own IT and security teams. Some firms are rushing ahead unsecured while others are stuck waiting on security. Neither is a healthy way to adopt a technology.

The risk grows once agents start acting on their own. An OutSystems report found that enterprise executives are confident they can keep their AI agents in line, but the room goes quiet when they are asked what happens if two agents disagree. Elsewhere, technology leaders in the region are learning to trust AI agents, but not with production. That caution is sensible, and it is also an admission: the controls most firms rely on were designed for people, not for software that makes decisions.

The other half of the problem is us. Many incidents begin with something ordinary: a reused password, a convincing email, or an employee pasting a supplier contract into a free chatbot to save time. That is what shadow AI looks like in practice, and banning AI does not fix it. The phishing playbook is also changing. Check Point Research’s latest data shows phishers now want you to type the malware in yourself, and AI-written lures are eroding trust in the old cues staff were trained to spot.

Security spend should match AI spend

My view is simple. If we are investing seriously in AI, we should invest at least as seriously in securing it, and I would argue for more. Every agent we deploy is one more actor with access to data and systems. As AmanTrue’s Evin McMullen puts it, AI agents need someone standing behind them. That means identity, accountability and limits on what each agent can touch.

Over the past four weeks we have met companies approaching this from different directions. Three stood out: Dropbox, Yubico and Aleph. Each answers a different part of the problem, and I would encourage you to look closely at all three.

Some are building guardrails around the data itself. Dropbox, which has been careful to stay neutral in the ChatGPT, Claude and Gemini contest, has launched Dropbox Protect, and it deserves attention from anyone putting AI on top of company files. It is less about classic cyber defence and more about the question every AI rollout should answer first: what happens to our data when people run large language models over it? Protect, formerly Nira, shows who can access company files across Dropbox, Google Drive and Microsoft 365, so overshared content can be found and fixed before AI tools surface it. If your documents already live in Dropbox, have that conversation now, before shadow AI forces it on you.

Others are fixing access, and this is where I would start. Phishing, reused passwords and stolen credentials all end the same way: someone logs in who should not. A hardware security key such as Yubico’s YubiKey makes that far harder, because it is phishing-resistant and a stolen password alone is no longer enough. Yubico told us that some customers place orders of around 100,000 keys at a time, partly because of staff turnover and keys that are never handed back. That is the scale at which organisations are already treating access as a hardware problem. I believe a large share of today’s incidents could be prevented by securing access this way, and the same discipline should extend to the agents acting on our behalf.

Also Read: Singapore leads on security governance but struggles to enforce it, report finds

And a new generation of firms is going after segments the big vendors have underserved. Scouter’s entry into Singapore’s crowded cyber market is a recent example.

Aleph, launched on 13 October by Red Alpha Singapore and nuKudo, goes after the people side of the problem. It is a technology workforce provider whose portfolio spans AI, cybersecurity, cloud and DevOps, and it hires on aptitude rather than CVs. With security talent in short supply, that matters: you cannot buy protection you do not have the people to run. If your team is stretched, Aleph is a partner worth calling.

What a security twin looks like in practice

A security twin is a simple rule: every AI budget line gets a matching security line, approved at the same time and tracked by the same owner. It is not a fixed ratio, and I would not pretend one number fits every company. It is a discipline that stops security from being the step that blocks a launch or the afterthought that follows a breach.

In practice, I would start with four questions. Which AI tools and agents are running in the company today, including the ones nobody approved? Who or what is each one acting as, and what can it reach? Where does our data go when it is used with a model? And who is accountable when something goes wrong?

The last question matters most. UOB’s research finds that banks want AI agents to move money, as long as someone is accountable, and Penta’s report makes the wider point that cyber incidents are now about trust and leadership as much as firewalls. Boards will not accept “the agent did it” as an answer.

Each partner above maps to one of those questions. Dropbox Protect covers where data goes. Hardware keys from Yubico cover who or what gets in. Aleph covers the people to run all of it. The rest is governance, which costs less than a breach and, as security reports keep showing, often fails at the point of follow-through rather than intent.

The three partners at a glance

Dropbox Protect is a data access governance product. It gives admins one view of who can access files across Dropbox, Google Drive and Microsoft 365, shows which files are shared internally, externally or publicly, and flags overshared files with their owners. Teams can remove public or company-wide links in bulk, revoke access for external users and former employees, and transfer ownership. Policies can alert on or automatically fix risky sharing, such as confidential files shared with personal accounts, and an exportable log supports audits and access reviews. For AI, the point is to restrict overshared content before AI tools and large language models can surface it.

Yubico makes hardware security keys in which the private key is generated on the device and never leaves it, which is what makes them phishing-resistant. The range includes the YubiKey 5 Series, a FIPS-validated series for regulated sectors, the YubiKey Bio Series, the Security Key Series and the YubiHSM hardware security module. The keys support FIDO2, passkeys and PKI, work without a phone or network connection, and integrate with identity providers including Microsoft Entra ID, Okta, Ping and Google Workspace. YubiKey as a Service covers pre-registered keys, backups and global fulfilment for large rollouts. In May, OpenAI added an opt-in setting for ChatGPT and Codex that removes password sign-in and lets users choose a security key.

Also Read: Singapore’s AI adoption surges, but data complexity raises security risks: Report

Aleph is a technology workforce solutions provider formed by Red Alpha Singapore and nuKudo, launched on 13 October. Where a traditional recruiter places candidates, Aleph combines talent assessment, training, deployment, professional services and technology solutions in one model. Its offerings include Select, Train, Deploy, Assessment as a Service, an In-House Talent Engine, Staffing Plus, Custom Workforce Solutions and AI workforce transformation. The roles it covers span AI, cybersecurity, cloud and DevOps, software and data engineering and network infrastructure, including Forward Deployed Engineers. Candidates are assessed on aptitude and problem-solving rather than CVs.

What we are doing, and what we ask of you

At e27, we are running a Startup Clinic for a curated group of startups from the Cybersecurity Research Programme Office (CRPO) technology translation programmes. These are teams turning research into products, and the market needs more of them. We would like the wider ecosystem to meet them at the Demo Day: investors looking for early exposure to security, and enterprises willing to pilot technology built close to home.

If you build, buy or back technology, here is the ask. Treat security as part of your AI plan from day one. Put guardrails around your data before AI touches it, which is the case for Dropbox Protect. Secure access with hardware keys such as Yubico’s, so a stolen password is no longer enough. Close the talent gap with partners such as Aleph, rather than waiting for the market to produce more security engineers. And watch this space. In an AI world that changes every quarter, it is becoming one of the most important ones.

The post Equal spend: why AI budgets need a security twin appeared first on e27.

Google will spend US$15 billion on Malaysian digital infrastructure across 2027 and 2028, a sum it unveiled on Wednesday with Prime Minister Anwar Ibrahim in the room. The package bundles new data centre campuses with skilling, water and clean energy commitments.

The scale is the story. In 2024, Google pledged US$2 billion for its first Malaysian data centre and cloud region. The new figure is seven-and-a-half times larger, and it is compressed into just two years.

Also Read: Server sanctuaries or net-zero derailers? Southeast Asia’s data centre dilemma

It also carries something rarer in hyperscaler press releases: an explicit promise that Google will pay for 100 per cent of the power its campuses draw, plus the direct cost of connecting them to the grid. In a region where the data centre boom has begun colliding with household electricity bills and water anxiety, that line may matter more to Malaysians than the headline number.

Elmina is live, Negeri Sembilan is next

Google’s first Malaysian campus, at Elmina Business Park in Selangor, is already running. More campuses are planned in Selangor and in neighbouring Negeri Sembilan, and the Selangor capacity will also anchor a forthcoming Google Cloud region for enterprise customers.

Conspicuously absent from the map is Johor, the southern state that has soaked up much of the region’s data centre spillover from Singapore, along with much of the scrutiny over what Johor gets from hyperscale capital. By staying in and around the Klang Valley, Google sits closer to Malaysia’s enterprise and government buyers, and to the talent pool it says it is recruiting from.

Alphabet President and Chief Investment Officer Ruth Porat framed the spend as aligned with Malaysia’s development priorities, pitching computing capacity as the base layer for homegrown AI applications. That dovetails with Putrajaya’s AI Nation 2030 agenda, which assumes local compute will be available on tap.

Malaysia’s own pitch is hard to dispute: Google cites 99 per cent internet penetration and fast AI uptake across government, business and consumers. Digital Minister Gobind Singh Deo said the blueprint sets a high bar for sustainable, high-impact infrastructure.

37,000 jobs a year, with an asterisk

Google says construction in 2027 and 2028 will add an average of US$1.1 billion a year to Malaysia’s GDP and support more than 37,000 local jobs annually, generating US$378 million in yearly labour income.

The fine print tempers that. Only about 6,000 of those roles are in construction itself; the rest are the indirect and induced jobs that economic impact models tend to conjure. Divide the labour income by the headcount and it works out to roughly US$10,200 per job a year, hardly a windfall.

Also Read: Malaysia’s Budget 2027 hands startups US$110.4M but the bigger cheques go elsewhere

Once operational, the campuses are projected to sustain more than 9,000 jobs a year, paying on average 45 per cent above the national median. Data centres are famously lean to run, so most of those jobs will again sit outside the fence. Google says it already has a full on-site team at Elmina and is hiring technical and facilities staff for the rest, while more than 80 Malaysian firms, from civil engineers to caterers and waste managers, are in its supply chain.

The power bill pledge is the real headline

Energy is where data centre economics and politics meet. Southeast Asia’s AI server boom is already straining power supply, and governments across the region are wrestling with who pays when hyperscaler load lands on the grid.

Google’s answer has three parts. It will pay for all the electricity its campuses use and the cost of connecting them. It will bring more than 1 GW of new clean energy capacity to the National Grid, anchored by a collaboration with Tenaga Nasional Berhad under the Corporate Renewable Energy Supply Scheme, which lets companies buy green power through the national grid. And it will explore round-the-clock clean energy to plug solar’s gaps, on top of existing tie-ups with consortiums led by Shizen Energy-Solarvest and TotalEnergies.

The most telling detail sits near the end of the statement. From 2027, Google plans to fund community programmes that strengthen grid resilience and bring down electricity bills for households in the states hosting its campuses. Companies rarely pay to soften a problem they don’t believe exists.

Water gets similar treatment. Google announced two replenishment projects, a river basin stewardship programme for Sungai Buloh and Sungai Selangor and water resilience centres in schools, with volumes measured under the Volumetric Water Benefit Accounting method and audited by independent hydrologists.

What is missing is the denominator. Google did not say how many megawatts its Malaysian campuses will draw or how much water they will consume, which makes “1 GW added” and “water replenished” hard to weigh. Replenishment is an offset, not a reduction, a distinction water-tech startups chasing data centre clients understand well.

A crowded queue of hyperscaler cheques

Google is hardly alone in courting Malaysia. Microsoft committed US$2.2 billion to Malaysian cloud and AI infrastructure in 2024, AWS has pledged about US$6.2 billion through 2037, and Oracle announced US$6.5 billion for a public cloud region the same year. Across the region, AI infrastructure dealmaking hit a record this year as Singapore, Indonesia and Thailand chase the same capacity.

US$15 billion in two years puts Google well ahead of those announced figures, though hyperscaler pledges are notoriously hard to compare: timelines, definitions and what counts as “investment” vary. For Malaysia, the competition is the point. Each new campus strengthens its claim to be the region’s compute hub, even as its push to climb the semiconductor value chain shows how hard it is to turn volume into market share.

800,000 trainees, and the harder number

Google also unveiled four programmes to upskill 800,000 Malaysians between 2027 and 2031, aimed at educators and youth, micro-entrepreneurs, civil servants and vocational jobseekers, starting in the states that host its campuses.

Also Read: Malaysia’s digital economy’s second wave looks nothing like the first

Headcount targets are easy to announce and hard to audit. Putrajaya wants 300,000 AI jobs by 2030, and the gap between a training certificate and a paying role is where such programmes usually lose momentum.

For founders, the nearer-term upside is more tangible: a local Google Cloud region promises lower latency and data residency for AI startups selling to Malaysian enterprises and government. Whether the power and water promises hold will only be clear once the campuses start drawing load, and once Google tells Malaysians exactly how much.

The post Google’s US$15B Malaysia bet comes with a promise to pay its own power bill appeared first on e27.

For two years, the rules of AI fundraising were simple: whoever asked for the biggest number got it. Last week, that stopped being true at the very top of the market.

OpenAI cut its fundraising target by US$10 billion to US$20 billion as falling tech stocks and weaker investor demand complicated its plans ahead of an anticipated 2027 listing. The valuation pitch slid to around US$1.25 trillion from an earlier US$1.4 trillion, and the US$11 billion secured so far is less than half the original goal. One description of investor demand that did the rounds was a single word: “thud.”

Also Read: Thailand’s AI startup push gets OpenAI backing through new public-private accelerator

In the same week, Anthropic’s amended IPO paperwork showed the other side of the ledger. The company disclosed roughly US$895.4 billion in aggregate spending commitments under purchase agreements and leases, almost all tied to compute and infrastructure.

Read those two stories together and the message for Southeast Asia’s AI founders is uncomfortable: the cost of building frontier AI is still climbing, while the patience of the people funding it is not.

The froth was already thinner than it looked

Before anyone in Singapore panics about a cooling that started in San Francisco, it’s worth admitting that the region’s AI funding boom was narrower than the headlines suggested.

Southeast Asia’s native AI startups raised US$4.1 billion across 23 disclosed rounds as of July, more than double 2025’s US$2 billion across 41 rounds, according to Tracxn. Impressive, until you look inside. Kling AI’s US$2.8 billion Series D accounted for about 68 per cent of that total. Strip it out and 2026 falls to roughly US$1.3 billion, below last year’s full total, spread across 18 fewer rounds.

Geography is just as lopsided. Singapore attracted US$9.3 billion across 227 rounds, while Vietnam, Malaysia, Indonesia and Thailand combined raised less than US$40 million.

So this is a region where AI capital was already concentrating into fewer, bigger cheques in one city. When the investors behind the world’s largest AI rounds start negotiating harder, that pressure doesn’t skip Southeast Asia. It lands first on the mid-tier startup in Jakarta or Ho Chi Minh City that was hoping the halo would reach it.

Your gross margin is someone else’s debt service

Here’s the bit that matters more than valuations. Most Southeast Asian AI startups don’t train frontier models; they build on them. That makes the frontier labs’ balance sheets part of every regional founder’s cost structure, whether they’ve read the filing or not.

Also Read: Anthropic, DeepSeek and drones: 10 takeaways from the 2026 unicorn boom

Anthropic’s disclosure is instructive because it’s so explicit. The total includes US$321 billion in leases that haven’t started and US$265.6 billion in purchase agreements. Broadcom accounts for US$161 billion, Google for US$114.5 billion, Amazon for US$110 billion and Microsoft for US$45 billion, and the filing flags that the company relies heavily on a small number of providers. The company itself calls its approach “bold but sane.” Critics are less generous: New Constructs CEO David Trainer labelled such obligations “hidden debt.”

Whichever label you prefer, the logic is the same for OpenAI and every other frontier lab carrying similar commitments. Obligations of that size get paid out of revenue, and a growing share of that revenue comes from the API customers building on top. If capital gets more expensive or more cautious, the pressure to protect margins moves down the stack, through pricing, rate limits, or the quiet retirement of cheaper model tiers.

For a Singapore legal-tech startup or a Philippine customer-service AI company whose biggest cost line is model inference, that’s not an abstract risk. It’s a supplier with enormous fixed obligations and every reason to stop subsidising your growth.

Singapore’s savings are in this trade too

There’s a second Southeast Asian exposure that rarely gets discussed in startup circles: the region’s sovereign money is sitting near the top of the frontier AI cap table.

GIC led Anthropic’s US$30 billion Series G, and in May it co-led the next round while Temasek joined as a significant investor. That Series H valued Anthropic at US$965 billion. On the OpenAI side, a UAE investor group including MGX could lead the current financing.

That isn’t a scandal; sovereign funds are supposed to take long-term bets. But it does mean that how the frontier AI IPO window plays out over the next 12 months will shape the risk appetite of the same institutions that anchor much of Southeast Asia’s venture ecosystem. A bruising listing season in New York could make Singapore’s limited partners more cautious about AI risk everywhere, including in their backyard.

What a picky market rewards

None of this means the AI story is over. The demand is real. Anthropic generated US$33.2 billion in revenue in the first nine months of 2026, against US$2.5 billion a year earlier. OpenAI is still raising one of the largest private rounds in history. A cooler top of the market is not a collapse; it’s investors rediscovering that price matters.

One reading of the OpenAI reset is that bargaining power is shifting toward investors. Southeast Asian founders should assume that shift will travel, and act accordingly.

That means building on more than one model provider so a single price change can’t wreck a P&L. It means pricing on outcomes rather than passing through token costs that someone else controls. It means taking the fair round now rather than holding out for the 2025 multiple that a fading comparable once justified. And it means treating inference costs the way e-commerce founders once treated logistics: as the line item that decides whether the business works at all.

Also Read: Anthropic’s Singapore MOU bets on safety testing, not consumer reach

The founders who come through this well will be the ones whose gross margins can survive their suppliers getting greedier.

The case against worrying

To be fair to the optimists, there are good reasons to read last week differently. A trimmed US$20 billion round is still enormous. Compute commitments stretching into the 2040s are not due tomorrow, and revenue growing at that pace can cover a lot of obligations. Competition between labs could just as easily push API prices down as up, which would be a gift to every startup building on top. And some of the cooling is plainly about public tech stocks falling, not about AI itself.

Those are serious arguments. But they all describe a market where Southeast Asia’s AI startups are price-takers, not price-setters. When the people at the top of the stack start counting their money more carefully, the sensible response at the bottom is to count yours.

The post The frontier AI money is getting picky. Southeast Asia’s AI founders should price that in now appeared first on e27.

SINGAPORE: The aisles around the Taiwan Pavilion at SWITCH 2026 were busy. At Booth Q01 of Sands Expo and Convention Centre, 33 Taiwanese startups spent three days, from 27 to 29 October, meeting visitors, investors and potential partners from across the region.

Among the visitors was Dr Tung Chen-yuan, Representative of the Taipei Representative Office in Singapore, who walked the pavilion and later spoke to e27 about what he saw.

His outlook was an optimistic one. Provided the founders find the right partners, he said, “I believe in the near future they will become very strong companies in the world.”

Also Read: Startup Island TAIWAN leads 33 deep-tech startups to SWITCH 2026, advancing their expansion into Singapore

The pavilion was led by Startup Island TAIWAN, the national startup brand backed by the National Development Council. e27 has covered the full line-up. This story is about what Dr Tung and seven of the founders told e27, and what happened at the booths.

Walking the floor

Asked on camera what he sees in these founders that tells him they will go far, Dr Tung started with the size of the problems they have chosen. “All of them are trying to tackle many difficult issues, including AI, energy, climate change and healthcare,” he said. “All of them are quite innovative and quite efficient.” He expects them to find partners quickly: “I believe they can find a lot of partners in Singapore.”

What happened at the conference

The pavilion was built for meetings, and the three days produced some. Several startups held investment talks with Openspace Capital, Qualgro Ventures and Anatres Ventures. Two more are discussing testing partnerships with a global semiconductor company and a Singapore engineering group. Two signed MOUs, one with a Singapore company on software licensing and hardware distribution and one with a South Korean company on glasses-free 3D content. Others plan to set up in Singapore, using StageOne, Singapore’s official startup platform, to find sites to test their products.

Some of the work was simpler: finding partners, one at a time. MantaGO is an omni-channel AI chatbot that connects social media and e-commerce marketplaces, and its maker reports 2,280 clients worldwide and 25 distributors. Oriana Lau, who heads up the Singapore market, said customers worry that AI can feel too robotic, so the team fine-tunes the bot to sound more human. For Southeast Asia, the plan is to treat Singapore as a kickstart and grow mainly through partners, with a target of 52 partners across the region by 2027, about one a week. “Our key focus is to acquire more partners and accelerate the usage of our solution with small and medium enterprises in the region,” she said.

Innovative, efficient and already international

On what he hopes for them five and ten years from now, Dr Tung talked about experience rather than potential. “All of them have a lot of international experience,” he said. “They already have a lot of clients in this region and in some international markets. So I believe they are quite capable. They are quite outstanding.”

His condition is partnership. As long as the teams find the right partners, he said, he sees them becoming “global players” in the near future, with Singapore and Southeast Asia as the first step in building those relationships, with Singapore being an important hub for the startups’ global ambitions.

LongGood is one example of that reach. The digital rehabilitation company already has customers across Taiwan, Japan, Singapore, Thailand and Hong Kong. In Singapore it wants to connect with hospitals and daycare centres. Ray Chen gave the long-term ambition in one line: “We aim to become the go-to brand for rehabilitation.”

Taiwan’s strengths, in his words

On what Taiwan can offer Southeast Asia that nobody else can, Dr Tung started with the relationship and then moved to capability. “Taiwan and Singapore have a very strong foundation of partnership and friendship in the long term,” he said. “Taiwan currently is very strong at advanced manufacturing capability, including semiconductors and AI-related products. All these companies are building their capability based upon Taiwan’s strengths, so they can bring a lot of integrated ICT capability to cooperate with partners in Singapore and Southeast Asia.”

Also Read: Taiwan Pavilion at SWITCH 2026: Meet the 33 Taiwanese startups heading to Singapore

He gave two examples. In healthcare, he said, companies are using AI and semiconductors to work out how to address people’s health concerns, and “Taiwan has a lot of advantages in this regard.” He sees a match with Singapore’s healthcare sector, which he described as full of innovation. In energy, he said, the same tools are being used to manage supply and demand.

EpiSante Biomedical, a liver cancer screening company, fits the healthcare case Dr Tung described. Its founder, Lance Lu, said Asia accounts for 70 per cent of liver cancer cases globally, and that the company’s patented test detects early-stage cancer with 91 per cent accuracy, against 10 to 20 per cent for conventional methods. The company plans to set up a headquarters in Singapore and then expand to Malaysia, Thailand and Vietnam through hospitals and clinics. “We’re looking to build an office here, a headquarters here,” Lance said.

Apollo Power covers the energy case. The company sells AI-driven energy management, and its founder, Sun Hsieh, said the system lets customers track their carbon goals, and that AI data centres are being built quickly across Southeast Asia and need stable power. The plan is to start in Singapore, move into Malaysia, Vietnam and Thailand, and be working with data centres or factories within six to 12 months. “Show real savings, then we scale,” Sun said.

On the AI side, futurenest builds software to govern enterprise AI. Its Xerno product controls what the AI knows and its Xparse product controls how it acts, which matters to regulated industries. The company reports 12 signed enterprise customers in active deployment across telecom, national defence and national transport. Founder Bob Hsu said the company’s edge is strong technology, and that its clients in Taiwan already include government agencies, financial institutions, healthcare providers and manufacturers.Hsu also said the product was picked this month as one of the world’s top 40 in a global NVIDIA competition. “We hope everyone can use AI safely and with peace of mind,” Hsu said, because AI today comes with too many governance problems and budgets that overrun, which leave users constrained. 

Why Singapore comes first

Dr Tung gave his own reason. “Singapore will be a very important first step for them to build their international partnership,” he said, “because Singapore is a hub of logistics, finance and talent. So this would be a very important step for them to go abroad.”

Hsu gave the most detailed answer from the founders. The company serves many large clients in Taiwan, and sees many top-tier enterprises in Singapore that can afford a high-level product. The plan is to set up a company and local team in Singapore, build reference cases there, and then add distributors and resellers across Southeast Asia. Hsu is looking for two kinds of partners: those who sell to AI data centres, whether in hardware, software or integration, and those who sell software or do system integration for medium and large enterprises.

Speculum AI builds a runtime layer for stable, controlled AI in production and targets regulated industries such as finance, banking, insurance and government. Within six months of entering the market, it secured an enterprise proof of concept with a major Japanese system integrator, according to the company. Dominique Tu, co-founder and CEO, said Singapore is “our most important stepping stone for Southeast Asia”, because it treats governance and AI stability as the core of deploying AI. The company aims to incorporate in Singapore within 2026.

Also Read: Taiwan bets on Gen Z founders to move beyond its chip-supplier image

eCloudEdge works where Taiwan’s manufacturing strength meets software. Rick Peng, CEO and founder, called Singapore the hub of Southeast Asia and the place to find a partner. He said the company governs operational technology (OT) and IT data, and wants a Singapore partner with both OT knowledge and software skills. “If you are purely on the OT side, you may focus on the OT application,” Rick said. “But we need someone with a software background.” Customers can deploy on premises or in the cloud, combined with ISO 27001 for security.

Startup Island TAIWAN describes the division of roles as Taiwan developing and validating the technology and Singapore serving as the base for commercialisation and expansion across Southeast Asia, with room to deepen cooperation in trustworthy AI governance and physical AI, drug validation standards and a clean energy ecosystem. The pavilion has closed. Dr Tung’s test for what comes next is whether the partnerships started in Singapore carry these companies abroad.

The post Taiwan’s envoy says its startups are capable enough to go global, with Singapore as the first step appeared first on e27.