Most small business owners I speak to compare website quotes the same way: they line up the first invoice and pick the lowest. It feels sensible. It’s also how many end up paying several times more than they planned.

In October 2026 we looked at the published prices of popular website packages in Malaysia and added up what each costs over three years: the setup or package fee, plus every renewal or monthly fee in that time. The difference between the first invoice and the three-year total depends almost entirely on how the package is billed.

One-off packages stay close to their sticker price. A five-page package priced at RM1,980 (about US$475) with a RM462 (about US$111) renewal from year three totals about RM2,442 (about US$586) over three years: 1.2 times the first invoice.

Monthly plans aren’t bad value in themselves. They usually bundle hosting, maintenance and edits that a one-off package charges for separately or not at all. The problem is comparing a one-off price against a monthly plan’s setup fee, as if they were the same thing.

Three habits fix most of this:

• Turn every quote into a three-year number. Ask: what do I pay in year one, year two and year three, and what does each payment include? Put the totals side by side.
• Check what happens to the domain. Promotional domain prices usually apply to the first year only. .my domain offers advertised at a few US dollars typically renew at around US$25 to US$39 a year, and Malaysia’s domain registry raised .com.my prices by RM5 (US$1.20) in March 2024. Make sure the domain is registered in your company’s name, so you can leave without paying to get it back.
• Ask what you can’t do without paying. Text changes, new photos, an extra page, a second language. A plan that includes them may be cheaper over three years than one that bills each change separately.

Also Read: Malaysian pension fund KWAP moves to contain damage after eFishery fraud shock

There’s a second cost that rarely appears on a quote: a website nobody can find. A site that isn’t indexed by Google, or doesn’t track which enquiries it brings, can be cheap and still poor value. When you compare quotes, ask how the provider will check that your important pages appear in Google and how you’ll know what the site brings in.

None of this needs expertise, just a spreadsheet and three questions. The businesses that get burnt are rarely the ones that paid the most; they’re the ones that compared the wrong number.

Prices are taken from providers’ published price pages, checked on 19 October 2026. Three-year totals are shown before SST.

—

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 Malaysian SMEs are comparing the wrong number when they buy a website appeared first on e27.

Washington has spent the past year making it harder, pricier and more nerve-racking for foreign talent to work in America. Asian founders, if a new survey is to be believed, have mostly shrugged.

Singapore-founded immigration platform Beyond Border’s August 2026 Founder Mobility Pulse found that 82 per cent of respondents had not changed their US expansion plans despite immigration uncertainty. Another 11 per cent were either bringing their timelines forward or exploring other visa routes.

Also Read: Europe’s trade truce with America has not restored trust in American business

On its face, that is a show of founder grit. It also comes from a company whose business is helping founders get US visas, which is worth keeping in mind before anyone treats it as a verdict on the whole region.

Customers, not capital

The headline motivation is commercial. Some 85 per cent of the 307 founders surveyed, more than 200 of them based in Asia, said access to US customers was their main reason for expanding. Only 8 per cent pointed to fundraising or investor networks, with talent and partners (5 per cent) and compliance (2 per cent) trailing.

The timelines are aggressive. Sixty per cent plan to land in the US within 12 months and a further 25 per cent within two years.

That customer-first logic makes sense, but the sample explains part of it. Roughly 83 per cent of respondents are at seed (35 per cent), bootstrapped (28 per cent) or pre-seed (20 per cent). Only 5 per cent have reached Series B or beyond.

Companies that have never raised institutional money are unlikely to cross the Pacific for American VCs. For them, the US is a sales territory, not a cap table strategy. Singapore’s MetaOptics, which is eyeing the US with a potential US$10 million warrant facility, is closer to the later-stage exception than the survey’s typical respondent.

A crackdown that hits some doors harder than others

The policy backdrop is not subtle. Late in August, the Trump administration proposed a US$103,265 annual fee for new H-1B visas, following an earlier US$100,000 fee that a court struck down. The report also notes recent moves affecting green-card sponsorship at major tech companies and investigations into university visa programmes.

Beyond Border’s reading of US Citizenship and Immigration Services (USCIS) data suggests the squeeze is uneven. Between FY2022 and FY2024, approved L-1 petitions, the route companies use to transfer staff, fell 31 per cent, while O-1/O-2 approvals for people with “extraordinary ability” rose 1 per cent. Between FY2023 and FY2025, EB-1A green-card approvals climbed 20 per cent, while EB-2 National Interest Waiver (NIW) approvals fell 39 per cent.

There are two caveats. The comparison windows differ by pathway, and none of the series fully captures the latest round of tightening. Approval counts also measure what the government granted, not what founders wanted.

Also Read: Taiwan’s startup talent problem is a matching problem, not a shortage

Then there is the inquiry data. Beyond Border analysed 1,140 immigration inquiries from Asia-based individuals between August 2025 and September 2026 and found that about 45 per cent (510) concerned the O-1 visa. That is a useful signal, but O-1, EB-1A and EB-2 NIW are also the pathways Beyond Border says it specialises in. A platform built around a visa will attract inquiries about that visa. e27 has previously explored why Beyond Border sees visas as part of the founder playbook; this survey is, in part, an extension of that pitch.

The founder as a one-person beachhead

The most telling finding concerns who actually makes the move. Thirty-five per cent expect the founder to relocate alone, and 29 per cent expect the founder to go with a small launch team. Only 16 per cent plan to put a US-based hire on the ground instead.

That is lean, and potentially punishing. A founder who sells in New York by day while managing a team in Jakarta or Bengaluru by night is running two companies across a 12-hour time difference. The report raises the right question but leaves it unanswered: when does a founder-led foothold need to become a real local operation? Anyone who has faced cross-border due diligence questions will know that American buyers and investors eventually expect more than one person and a co-working desk.

Asia keeps the engine room

For Southeast Asia, the more consequential number may be 72 per cent: the share of founders who intend to keep engineering and product in Asia. Many also plan to keep operations (40 per cent), sales and marketing (30 per cent) and senior leadership (20 per cent) in the region.

That supports the region’s engineering employers, but it also highlights an old pattern. When customers and revenue are in the US while code is written in Asia, cost arbitrage often explains as much as talent strategy. It also adds pressure to a pipeline already under strain from AI, as the junior developer dilemma makes clear.

The returnee numbers are real, but modest

The report’s final section looks at talent flowing the other way. Recruitment firm Xpheno estimates that 12,700 white-collar tech professionals returned to India from the US in the first nine months of 2026, compared with 15,100 in all of 2025, 9,800 in 2024 and 7,200 in 2023. At the current pace, 2026 would come in at around 17,000, which is a rise but not an exodus.

For scale, the US National Science Board says India and China accounted for 29 per cent and 12 per cent respectively of foreign-born workers in US science and engineering occupations in 2024. Beyond Border acknowledges that this says nothing about how many might actually leave.

The report names Singapore as a potential beneficiary. That is plausible, but returning talent has a habit of leaving again. Vietnam has already discovered that keeping returning engineers is harder than attracting them.

Also Read: Malaysia wants 300,000 AI jobs by 2030. Talent will decide if it gets there

Beyond Border’s own conclusion is the most defensible line in the report: tighter US immigration policy alone will not bring talent back. Pay, career progression and serious work in fields such as AI will. On that front, the US still competes better than most of Asia, whatever happens to the visa fee.

The post Visa fees are soaring, but founders want US revenue more than US investors appeared first on e27.

I run OTSATE, a small lemongrass sauce brand in Malaysia. I’m the founder, the cook and the person who does the numbers, so everything below is one operator’s experience, not a benchmark.

Most of my admin now runs through Claude Code, an AI agent that works inside a terminal and can read my files and write to my spreadsheets. I’m going to describe one task it does well, one moment it got badly wrong, and the one column it will never fill in.

The task it now owns: Monday numbers

Every Monday I give it four things. Sales last week. What I spent on ingredients and packaging. Cash in the bank today. The decision I’m making this week.

It writes the row into a Google Sheet and works out the rest: revenue per day, food cost as a percentage of sales, and how many weeks of fixed costs the week’s sales would cover. A small script it wrote reads my bank statement PDFs and sorts each transaction into a category. Every category it guesses gets flagged until I confirm it, because it cannot tell a lemongrass supplier from a water bill without being told once.

The honest reason this works: I avoided my own trackers for months. Typing a bad week into a cell felt like a verdict on me. Saying “sales were low” out loud to a terminal and watching the row app still get recorded. That is the whole point.

What I stopped doing is opening the spreadsheet to type. What I do with that time is read it. Ten minutes, every Monday, and I actually look.

Also Read: I used AI to rebuild my websites without knowing how to code

The moment it was confidently wrong

In July I asked it to cost a new product: Kam Heong sauce in 100g retort pouches, five for RM45 (about US$10.80). I gave it the recipe and asked for the contribution margin.

It came back with a clean, well-formatted answer. Contribution of 12 to 19 per cent. Reprice to RM60 to RM65 (about US$14.40 to US$15.60) for five, or don’t launch.

I nearly believed it. The table looked like something a consultant would charge for. Then I read the cost lines.

It had priced every ingredient at what you’d see on a Malaysian supermarket shelf or an online grocer. Dried shrimp at RM90 (about US$21.60) a kilo. I pay RM37.50 (about US$9) at the wet market. Oyster sauce at RM21 (about US$5.04) a kilo. I pay RM4 (about US$0.96) in bulk. Fresh chilli at RM14 (about US$3.36). I pay RM5.63 (about US$1.35). Every single line was high, most by double or more, and the errors compounded through the recipe. It also added a per-pouch fee for outsourced retort processing. I own the retort machine.

With my real prices the contribution was 61 per cent. That’s all. The model wasn’t lying. It never asked where I shop, and I never told it, and it filled the gap with the most visible number on the internet.

In late September it did a smaller version of the same thing. It suggested I fry my own garlic instead of buying it fried, to bring food cost down. On paper, correct. But I don’t draw a salary. My labour appears nowhere in the P&L. Buying fried garlic at RM5 (about US$1.20) per 100g is how I force my own time into the ingredient cost so the pricing stays honest. The tool optimised the number it could see and ignored the one it couldn’t.

What changed about how I check its work

I gave it a standing rule, saved in the memory file it reads at the start of every session: never estimate my costs. Every line in a costing is either a price I supplied or marked OPEN. No recommendation on top of an OPEN line. If the answer depends on something it doesn’t know, it has to say so and ask.

The second rule is on me. I read the inputs before the conclusion now. A margin figure is only as true as the ugliest line underneath it, and the tool will format an ugly line just as nicely as a true one.

Also Read: How AI became my sparring partner

The column it can’t fill

The Monday sheet has a column called “the decision this week”. It is always blank until I write in it.

In August I cancelled a December launch for a nyonya achar I had been testing in five-kilo batches. The costing was fine. The product was good. I cancelled it because every jar needed days of sun and hours of my hands, and when I pictured making a thousand of them I did not want that life. No spreadsheet carries that information.

If I let the tool decide, it would pick whichever option lines it had. The costing story above is exactly why that’s dangerous: the lines it has are not always the real lines. And it has no stake. It will not be the one at the stove in December.

What shifted with people

My husband sits as an informal advisor on the business. The Monday sheet has a column for the one question I’m bringing to him that week. Our conversations got shorter and more specific, because the numbers arrive before the feelings do. The friction is that he can now see a bad week in a cell, same as me. We are both still learning to talk about that.

If you run something small

Three things from doing this.

Give the tool your real inputs or it will invent plausible ones. Plausible is worse than blank.

Let it do the typing and keep the reading for yourself. The reading is where the business is.

And write down the decisions it is not allowed to make. Mine fit in one column. Yours probably do too.

—

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 AI told me to raise my prices by 40 percent but it had never been to a wet market appeared first on e27.

Bitcoin has climbed back above the critical US$81,000 baseline. This 24-hour rebound does not erase a difficult month. The digital asset remains in its weakest October performance in six years, down roughly 4.2 per cent, while conflicting forces keep price action in a sideways range. The market has spent a long time without a decisive break. That pattern reflects a fierce tug-of-war between strong rebound catalysts and firm upward caps, all against severe macro headwinds and a looming inflation report.

The first support comes from the Federal Reserve. Risk appetite expanded after the central bank voted 10-2 to leave the federal funds target range steady at 3.75 to 4.00 per cent. Avoiding a pre-election liquidity shock triggered an immediate relief rally. Resilient institutional inflows add another layer of stability. Spot US Bitcoin ETFs pulled in US$110.34 million in net inflows on October 26, followed by another US$176 million on October 27. That steady backing provides a firm price floor.

Several forces actively suppress upward movement. Options traders brace for dramatic swings as a massive US$11 billion options expiry closes this Friday. With 70 per cent of dealer hedging sensitivity, known as gamma, concentrated heavily between US$75,000 and US$82,000, market makers actively suppress large expansions. US demand has turned noticeably soft.

The Coinbase Premium Index, which measures the gap between US and offshore spot buying, has not posted one positive day all of October and hit its lowest levels since June. Technical resistance failure compounds the issue. On-chain analysts note that Bitcoin recently suffered a clear fakeout near the US$83,000 mark. Failure to reclaim and hold this baseline keeps technical day traders on the defensive.

Also Read: The Fed decides today: Here are the 2 levels every Bitcoin trader should watch

Bitcoin is defying significant macro headwinds. While the Fed paused, Chair Kevin Warsh shifted language to suggest the economy is expanding strongly rather than solidly. At the same time, two policymakers dissented in favour of an immediate hike, pushing market odds for a December rate increase up to nearly 75 per cent.

Crude oil and yield pressures loom large. Brent crude remains stubbornly elevated at US$100 to US$111 per barrel amid ongoing Middle East war headlines. This energy shock keeps the 10-year US Treasury yield near a multi-decade high of 5.1 to 5.3 per cent, intensifying competition for non-yielding assets.

Midterm election de-risking adds another layer. Capital is aggressively de-risking ahead of the November 3 US midterm elections. A highly polarised outlook dictates vast price variances. Institutional consensus projects year-end targets of US$90,800 under a unified Republican Congress, versus US$76,450 if Democrats sweep both chambers.

The immediate catalyst is the September 2026 Personal Consumption Expenditures price index report, the Federal Reserve’s preferred inflation gauge. Economists project a sharp acceleration in price pressures. The US Bureau of Economic Analysis releases the data at 8:30 AM Eastern Time (8:30 PM Singapore Time).

Consensus models anticipate a broad-based monthly and annual tick-up. The September 2026 forecast for the headline PCE price index shows a +0.5 per cent month-over-month increase, up from the +0.3 per cent actual in August. The year-over-year forecast stands at +3.7 per cent, or roughly 3.6 per cent, up from the 3.4 per cent actual in August.

Analysts forecast the core PCE price index, which excludes food and energy, to rise +0.3 to 0.32 per cent month-over-month, up from the +0.2 or 0.25 per cent actual in August. Annually, core PCE will likely rise +3.1 per cent, up from the 3.0 per cent actual in August.

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

Several key drivers explain the expected jump. Higher crude oil and natural gas prices, driven by supply disruptions in the Middle East, pushed energy costs up. Oxford Economics estimates PCE energy prices rose roughly 5.4 per cent in September alone. Persistent core services and goods remain sticky.

Following hot Consumer Price Index and Producer Price Index releases earlier in the month, core categories have proven stubborn. Bank of America analysts noted that price pressures span both core goods and core services. Higher input and component costs tied to ongoing corporate artificial intelligence buildouts continue to filter into the computer software, hardware, and accessory sub-indices.

This report drops less than 24 hours after the Federal Open Market Committee voted 10-2 to hold interest rates steady at 3.75 to 4.00 per cent. A hot PCE print confirming headline inflation running at 3.7 per cent and core at 3.1 per cent, well above the Fed’s stubborn 2 per cent target, will validate the two dissenting votes for an immediate hike. Interest rate futures have already priced in roughly an 85 to 91 per cent chance of a rate hike by December.

An upside surprise in today’s PCE figures could cement expectations that the central bank will resume tightening sooner than risk assets prefer. A hotter-than-expected print will likely lift US Treasury yields, keeping the 10-year near 5.0 per cent or higher, pressure non-yielding assets like Bitcoin and gold, and spark immediate turbulence across equity index futures and the USD/EUR exchange rate.

Also Read: 92% of Bitcoin liquidations were shorts: What happens when the squeeze ends?

Global financial markets closed slightly higher after the Federal Reserve split on keeping the benchmark interest rate steady. The S&P 500 rose 0.28 per cent to 7,743.98. The Nasdaq Composite gained 0.27 per cent to 26,549.85. The Dow Jones Industrial Average added 0.19 per cent to 52,475.10. Major US indexes posted their third straight daily gain during regular hours but stayed mostly range-bound. The initial relief rally has given way to sharp divergence and tech-sector volatility in early-morning extended trading as investors closely scrutinise mega-cap AI spending.

In early Asian trading and overnight futures, Nasdaq 100 futures have dropped over 1 per cent. The Federal Open Market Committee voted 10-2 to keep interest rates unchanged, as expected. The tone was subtly hawkish. Governor Lisa Cook and Cleveland Fed President Beth Hammack dissented, actively voting for an immediate 25-basis-point hike.

The Fed also upgraded its economic assessment to “expanding at a strong pace,” up from “solid,” while noting that inflation remains elevated. Chair Kevin Warsh avoided forward guidance but emphasised readiness to act. The CME FedWatch tool now shows an 85 per cent probability of a December rate hike. Warsh announced an adjusted 2027 calendar, reducing rate-setting votes to seven of eight annual meetings.

In my view, Bitcoin sits at a precarious intersection. Resilient institutional inflows and aggressive macro headwinds define the current sideways range. The US$81,000 level is a temporary resting point, not a sign of strength. The impending PCE report and the massive options expiry will likely dictate the next leg of volatility. The token has gone too long without a decisive break, and until the Fed pivots or Bitcoin breaks decisively above US$83,000, it remains hostage to macro data and derivative positioning.

I believe, and I said that yesterday, that the path of least resistance remains downward in the near term, given hawkish Fed rhetoric and technical failures, while the long-term debt-devaluation narrative provides a formidable floor. The September PCE report will serve as the immediate arbiter, determining whether Bitcoin can sustain this rebound or succumb to mounting pressure.

—

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 rebounds to US$81,000 but faces weakest October in 6 years appeared first on e27.

I use AI almost every day. Sometimes, several times a day.

As someone managing a digital publication and overseeing marketing for an esports business, I’ve come to rely on ChatGPT (and sometimes Claude) for everything from writing and research to campaign planning and website troubleshooting.

It’s saved me countless hours, particularly when it comes to producing first drafts, organising information and working through tasks that would otherwise take considerably longer.

But there’s one thing I’ve learnt from using AI so extensively: just because it sounds confident doesn’t mean it’s right. And the more useful AI becomes, the easier it is to forget that distinction.

When AI gives you an answer that looks perfectly reasonable

One of my more memorable experiences involved something completely unrelated to work: checking Singapore’s TOTO lottery results. I’d asked ChatGPT to help me check the winning numbers against a particular set of numbers on a regular basis.

It seemed like a straightforward task. The results are publicly available, and comparing two sets of numbers hardly requires complicated reasoning. Especially when I had provided the URL to the results page. Except that there were several occasions when the results it provided were incorrect.

What made this frustrating wasn’t simply the mistake. It was the way the information was presented, as though it were factual and verified. I had to question the results and check them against the official source.

It might sound like a trivial example, but it illustrates a much bigger problem. If AI can confidently get something as straightforward as lottery results wrong, what happens when we’re using it to research business opportunities, interpret industry developments or prepare information that other people will rely on?

The issue isn’t necessarily that AI makes mistakes. People make mistakes too. It’s that AI can present inaccurate information in language that gives us very little reason to doubt it.

The more you use AI, the more tempting it becomes to trust it

When I first started using ChatGPT regularly, I approached its responses with a fair amount of curiosity and scepticism. I’d read through the output carefully, compare it with what I already knew and make adjustments.

But once you’ve used a tool successfully hundreds of times, it’s natural to become more comfortable with it. You start recognising how much time it saves. You learn which prompts work. You become accustomed to receiving useful answers almost immediately. And that’s where complacency can creep in.

For The Wellness Insider, the digital wellness publication I run, I frequently use AI to help turn press releases into articles, prepare SEO information and adapt content for social media. It’s an incredibly useful starting point, but I still need to ensure that names, dates, statistics and claims are accurate. A well-written article containing incorrect information is still an incorrect article.

As a publisher, I’m responsible for what appears on my platform. I can’t simply blame ChatGPT if something inaccurate gets published. That responsibility doesn’t disappear because the first draft was generated by AI.

Also Read: Muggles struggle, wizards juggle: What happens when everyone gets an AI familiar

Sometimes, the problem isn’t accuracy. It’s continuity

Another frustration I’ve encountered is AI losing track of instructions during longer projects. This has been particularly noticeable when working on website development and marketing campaigns.

It’s a strange experience because AI can be remarkably capable at solving a technical problem, yet occasionally struggle to maintain continuity across an extended project. The challenge is that its conversational nature creates an impression of shared understanding.

When you’ve spent hours discussing a project, you naturally expect the other party to remember what you’ve agreed upon. But AI doesn’t necessarily retain or apply every instruction consistently, particularly when a conversation becomes lengthy or complex.

I’ve learnt that important decisions sometimes need to be restated, and completed work needs to be verified rather than assumed. In practice, that means treating AI-generated work as something that requires review, not as a finished deliverable simply because the system says it’s done.

AI can help me make decisions, but it shouldn’t make them for me

One of the ways I find AI most valuable is as a sounding board. When I’m considering a sponsorship proposal, developing a marketing campaign or deciding how to position a service, I can use ChatGPT to explore different approaches. It can help me identify potential weaknesses, organise my thinking and consider alternatives I might have overlooked.

But it doesn’t know everything about my businesses. It doesn’t have the same understanding of my relationships with clients, the nuances of a particular negotiation or the practical constraints I’m dealing with at any given moment.

For example, AI might suggest an ambitious marketing campaign with multiple content formats, outreach activities and promotional channels. On paper, the strategy might look sensible. But if I’m the person responsible for executing most of it, I have to consider whether it’s realistic. A campaign that requires more time, money or manpower than I have available isn’t necessarily a good campaign, regardless of how impressive the plan looks.

AI can help me evaluate options. It can’t decide which trade-offs I’m willing to make. That’s still my job.

Also Read: How we use AI to triage inbound B2B leads without blasting everyone the same way

I’ve learnt to manage AI rather than simply use it

The longer I’ve worked with AI, the more I’ve realised that getting value from it requires a certain amount of management. I need to provide clear instructions, explain the context, establish expectations and review the results.

When something is inaccurate, I have to correct it. When the output doesn’t fit my brand, I have to refine it. And when it makes a recommendation that doesn’t make sense for my business, I need to be comfortable rejecting it.

In some ways, this resembles managing a junior colleague, although the comparison has its limits. A human colleague can develop contextual understanding through experience, take responsibility for decisions and recognise consequences in ways that an AI system cannot. AI also doesn’t necessarily know when it’s wrong.

That’s why I think one of the most important skills for anyone using AI isn’t prompt writing. It’s judgement. You need enough understanding of the task to recognise when the output doesn’t make sense.

For factual information, that means checking reliable sources. For technical work, it means testing the results. For business decisions, it means evaluating the recommendations against your actual circumstances.

The amount of checking should also depend on the consequences of getting something wrong. A mediocre social media caption is relatively easy to correct. An inaccurate health article, broken website function or misleading business proposal could have much more serious implications.

I still wouldn’t want to work without it

Despite these frustrations, I remain an enthusiastic AI user. It’s made a meaningful difference to how I work, particularly as someone handling responsibilities that would ordinarily be spread across several people.

I can produce first drafts faster, explore ideas more easily and tackle technical projects that I previously wouldn’t have attempted on my own. It has also really helped me gather my thoughts and opinions when I’m doing a product or service review that may span over a few days or weeks. Those benefits are real. But so are the limitations.

I don’t think the solution is to stop using AI or to distrust everything it produces. That would mean ignoring a genuinely useful tool. Instead, I’ve become more deliberate about deciding when to rely on it and when to slow down and verify the output.

For routine drafting and brainstorming, I’m comfortable allowing AI to do much of the initial work. For factual claims, technical changes and important business decisions, I take a more cautious approach.

Ultimately, AI has changed how I spend my time, but it hasn’t removed my responsibility for the quality of my work. I may have gained an incredibly useful assistant, but I’m still the one who has to make sure it gets things right.

And that’s probably how it should be.

—

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 saves me hours, but I don’t trust it blindly appeared first on e27.

Part one of this guide ended with a draft. This one starts there.

A finished draft is a bit like a packed suitcase: the hard decisions are made, but you can still leave without your passport. Plenty of good contributor articles stumble in this last stretch, with an AI edit that sanded away the author’s voice, a stock photo of two people shaking hands, or an author profile so bare we’re not sure who wrote the piece.

So this part covers the journey from draft to published: how to use AI without handing it the pen, how to pick an image that earns its place, what to expect once you hit submit, and how to make your article keep working for you after it’s live.

Using AI to help you write

AI can make the writing process faster. What it can’t supply is the thing that makes a contributor article worth reading: your experience, your judgement and your point of view.

The simplest rule: use AI to help you think and write better, not to think and write for you. You remain responsible for the story, the argument and every claim in the final draft.

Brainstorming topics

Not sure what to write about? AI can help you turn your experience into article ideas, but only if you give it something to work with.

Instead of asking: “Give me 10 startup article ideas.”

Try: “I am a fintech founder in Singapore. We recently expanded into Indonesia and learned several unexpected lessons about customer acquisition, regulation and hiring. Based on this experience, suggest 10 article ideas for the e27 Contributor Programme. Focus on specific insights that would be useful to other founders rather than promotional topics.”

The first prompt gets you ideas anyone could write. The second gets you ideas only you could write, which is exactly what we look for.

Once you have an idea, AI is also useful for pressure-testing it. Ask it to challenge your angle, narrow a broad topic or find the question your experience can answer. For example: “Here is my initial idea: [idea]. What are three stronger or more specific angles I could explore? For each, explain why it would be useful to startup founders or operators.”

AI can help you find the story. Deciding which story is worth telling is still your job.

Research

AI is good at helping you work out what you need to know. For example: “I’m writing about Southeast Asian startups expanding into Japan. What areas should I research to understand the opportunities and challenges? Give me a research checklist covering market size, customer behaviour, regulation, competition, funding and talent.”

You can also use it to spot gaps in your knowledge, summarise material you’ve gathered, compare perspectives and flag claims that need supporting evidence.

What it can’t be is your source. AI can produce information that is outdated, wrong or entirely made up, and it often sounds equally confident either way. If a statistic is going into your article, don’t ask an AI tool whether it’s true. Find the original report and check it yourself. More on this under “Fact-checking” below.

Creating an outline

Once you have your idea and research, AI can help shape them into a logical structure. The key is to give it your argument rather than asking it to invent one.

For example: “My article argues that Southeast Asian startups should rethink how they approach expansion. My three main points are [point 1], [point 2] and [point 3]. Help me create a logical article outline. Don’t add new arguments. Suggest where I could use examples, data or my own experience.”

A good outline will show you where your argument has gaps, where you’re repeating yourself and what belongs in each section. If AI starts adding points you don’t actually believe, take them out. The argument has to be yours.

Improving clarity and flow

Already written a draft? This is where AI earns its keep.

Instead of: “Rewrite this entire article to make it better.”

Try: “Review this article for clarity and flow. Highlight sentences that are confusing, repetitive or overly complicated. Suggest improvements, but preserve my meaning, tone and point of view.”

The second prompt keeps you in control. You get specific suggestions to accept or reject, rather than a new article that happens to share your topic.

Then watch for AI polish. Left to its own devices, AI tends to make writing more formal, more generic and more predictable. Phrases such as “in today’s rapidly evolving landscape”, “game-changer”, “unlocking new possibilities” and “revolutionising the industry” can make an article sound like it was assembled by a committee of robots. Our editors read a lot of drafts, and this is usually the first thing we notice.

If an edit makes your writing sound less like you, don’t use it.

Fact-checking

AI is not your fact-checker. It can help you find the claims that need checking, but the checking is yours to do.

A useful prompt: “Review this article and list every factual claim that should be independently verified before publication. Do not determine whether the claims are true. Just identify what needs checking.”

Then go through the list:

• Numbers and statistics: Where did the number come from? Is the source credible and current?
• Quotes: Did the person actually say this, and is it in context?
• Company information: Are funding amounts, founding dates and product details correct?
• Dates: Are events, launches and reports dated accurately?
• Claims and predictions: Have you separated facts from opinions and assumptions?
• Sources: Have you gone back to the original, rather than another article’s interpretation of it?

Getting a second opinion

Once your draft is finished, AI can act as a second pair of eyes. For example: “Act as an editor reviewing this contributor article for a technology publication. Identify the three strongest parts of the article and the three areas that need improvement. Focus on argument, clarity, specificity and reader value. Do not rewrite the article.”

You can also ask it to suggest stronger headlines, point out weak sections, tighten long paragraphs or check whether your conclusion actually follows from your argument.

Treat its suggestions the way you’d treat notes from a well-meaning colleague: useful, occasionally wrong and never final. You are the editor of your own article.

Where to draw the line

AI can help you brainstorm, organise, outline, critique and refine. It should never:

• Invent your experience, quotes, statistics or sources
• Write an article from generic information with nothing of your own in it
• Turn promotional material into something that looks independent
• Disguise copied or repackaged content
• Be treated as the final word on facts

Above all, don’t ask AI to write the whole article and submit the result unchanged. A contributor article should contain something you know, have experienced, believe or have learned. That’s the part no tool can supply, and it’s the part our readers come for.

Also Read: The e27 Contributor Programme: Writing an article that gets published — Part 1

Keeping your own voice

The strongest contributor articles sound like a specific person, not a generic industry voice. A few ways to protect yours:

• Read your draft out loud. If it doesn’t sound like something you’d say to a colleague, it’s probably been smoothed into AI-speak somewhere along the way.
• Keep your quirks. Your sentence rhythm and the phrases you’d actually use are worth more than prose that’s merely cleaner.
• Rewrite the opening and conclusion yourself. If you leaned on AI for a first draft, these are the sections readers remember most, so they should sound most like you.
• Keep your uncertainty. Don’t let AI turn genuine nuance into false confidence. If you’re unsure about something, say so. It’s often what makes a piece feel real rather than templated.

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Creating images

Your image is the first thing a reader sees, often before they’ve read a word of your headline. A good one helps them understand your article at a glance and makes them want to read on.

It doesn’t need to illustrate every detail in your headline. The best images usually capture the idea, mood or central theme of an article rather than simply showing its subject. Think of yours as the visual introduction to your story.

What works for contributor articles

The strongest images do one of three things: illustrate a concept from the piece, set a tone that matches the argument, or give the topic a relevant visual anchor, such as a chart, a map, a workspace or a product in use.

What works well:

• A clean, conceptual illustration tied to your core idea, such as an open padlock for a piece about removing a paywall, or a bridge for one about regional connectivity.
• A real photo of a place, event or setting mentioned in your article, such as a city skyline, a conference or an office.
• A simple chart or data visualisation if your article is built on data.

What doesn’t:

• Generic stock photos of people shaking hands, pointing at laptops or standing in front of whiteboards. They add nothing, and readers have seen the same handshake on a hundred unrelated articles.
• Images with visible text, logos or watermarks that you don’t have permission to use
• Anything overly literal or clichéd. A robot hand reaching for a human hand has illustrated more AI articles than anyone can count.

Size and format

Use a widescreen image in a 16:9 aspect ratio. The recommended size is 690 × 390 px, which keeps your image aligned with the article layout and ensures it displays properly across the platform.

If you’re generating an image with an AI tool, set it to widescreen from the start. Cropping a square image afterwards almost always cuts off something important.

Before uploading

Give your image a quick final check:

• Nothing important is cut off, and key visual elements aren’t too close to the edges
• It’s sharp and high enough quality to look clean on both desktop and mobile
• There’s no unnecessary text or watermark

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Submitting your article

You’ve done the hard part. Submitting is the easy part, but a few small steps make the difference between an article that moves straight into review and one that sits waiting for missing details.

Step 1: Create your e27 profile

If you don’t have an e27 profile yet, you can sign up in a minute using your LinkedIn, Facebook or Google account. Once you’re in, don’t forget to verify your profile. It’s a quick step, but we can’t publish articles from unverified profiles, so it’s worth doing straight away.

Step 2: Complete your profile

Your profile is how readers know who wrote the article, so a complete profile is mandatory before any article can be published. Before you submit, make sure yours includes:

• A headline: your current role and organisation
• A bio: a few lines on who you are and what you work on
• A profile image
• Your location
• At least one social link, such as LinkedIn

It’s worth a few extra minutes. Your bio travels with every article you publish, and a good one is often what turns a curious reader into one who follows your work.

Step 3: Submit your article

When you’re ready, head to the Contributor Programme page and click “Start Writing”. From there, it’s refreshingly simple: paste in your article, add your tags and featured image, and hit submit.

Also Read: Write, connect, grow: What motivates us to run the e27 Contributor Programme

If someone is helping you submit

We know many contributors work with PR or communications teams, and that support is welcome. The one thing we do ask is that each article is submitted from the author’s own profile. It keeps every piece connected to a real person and makes sure the credit goes to the right name.

So if you’re helping an author, they’ll just need to do a one-time profile setup themselves. After that, they’re all set to submit.

Choosing your tags

Tags help the right readers find your article. Every contributor article is automatically tagged “Community”, and you can add up to four more from our verticals, five in total.

If you’re writing about how you use AI at work, for example, your tags might be Community, Artificial Intelligence and Productivity.

The best tags describe what your article is really about rather than every topic it brushes past. That way, readers browsing a vertical find exactly what they came for, and you find exactly the readers you wrote for.

Adding your featured image

You’ll find the option to upload your featured image in the text formatting toolbar, in the content body. If you’d like a quick refresher on size and format, the “Creating images” section above has you covered.

Originality and ownership

Your article is yours, and it stays yours. When you submit it to the Contributor Programme, you’re giving e27 a licence to use, edit and modify it, which is what lets us shape it for publication and share it with our readers.

One thing to keep in mind: once an article is published, we can’t remove it. So if anything still feels uncertain, whether a figure, a claim or a detail about your company, it’s well worth settling before you submit.

When you hit submit, you’re also letting us know that:

• It’s your own original work. The ideas, experience and argument are yours, even if AI helped along the way.
• Your images are credited. If your featured image or any visuals come from another source, a quick credit is all it takes.
• Your quotes and sources are genuine. Every quote can be traced back to a real source, and every statistic is cited accurately.

Contributor articles are published under your name and reflect your own views. That also means you’re responsible for what your article says, so make sure you’re comfortable standing behind every claim in it.

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After you submit

Once you hit submit, your article is in our hands. Here’s what happens next, so you’re never left wondering where it’s got to.

What happens after submission

Your article goes straight to our Contributor Programme editorial team. We read every submission to make sure it fits our editorial guidelines and house style, the same standards covered throughout this guide. Once it’s ready, we add it to our publishing schedule.

You don’t need to keep checking in. We’ll send you an email as soon as your article has been scheduled.

Making corrections

Once an article is submitted, it can no longer be edited from your side. But don’t worry if you spot a typo or a figure that needs fixing. Just email us at writers@e27.co with the details, and we’ll take care of minor corrections for you.

If your article needs revisions

Sometimes an article is almost there but needs a little more work. If that’s the case, we’ll email you with our suggested revisions.

To resubmit, you’ll need to send it in as a fresh submission. Your previous version is removed from the system once revisions are requested, so make sure to work from your own saved copy of the draft.

If your article isn’t accepted

Not every article will be the right fit for the Contributor Programme, and that’s okay. If yours isn’t accepted, you’ll find the reasons on your content dashboard. Many contributors use that feedback to shape a stronger piece next time, and we’d always love to see what you come back with.

Publishing timelines

Most articles are published within seven to 10 working days of submission. Once yours is scheduled, you can see the exact publishing date and time on your content dashboard.

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Promote your work and grow with e27

Publishing your article is a milestone, but it’s also a beginning. Here’s how to make the most of it, and of being part of the Contributor Programme.

Sharing your article

Nobody can share your article quite like you can. Your network already knows your work and trusts your perspective, which makes your own post one of the best ways to get your article in front of the right readers.

When your article goes live, share it on LinkedIn and anywhere else your peers spend time. A line or two on why you wrote it, or the one idea you most want readers to take away, usually lands better than the link on its own.

We’ll do our part too. Your article and company are highlighted across e27’s social media channels and in our daily digest newsletter, giving your work a second life with our wider audience.

Building your e27 author profile

Every article you publish is collected on your e27 author profile, so over time it becomes a living portfolio of your thinking. Keep your bio and headline up to date as your role changes, since readers often look you up after finishing an article they enjoyed. A strong profile turns a single read into a lasting connection.

Contributor Spotlights

Our Contributor Spotlight series features members of our community in more depth, giving readers a chance to get to know the person behind the byline.

Spotlights are chosen by our editorial team, based on factors such as how regularly you contribute and how your articles resonate with readers. If you’d like to be considered, you’re welcome to put yourself forward by writing to writers@e27.co. The final choice rests with our editors, but we always enjoy hearing from contributors who want to share more of their story.

Top contributors of the year

At the end of each year, we celebrate the contributors whose voices shaped the conversation through our top 27 contributors list. Our editors make the selection using similar factors to Contributor Spotlights, looking across your contributions over the whole year.

Contributor community activities

The Contributor Programme is more than a place to publish. Throughout the year, we run Writing Sprints: themed week-long challenges with daily prompts, designed to help you get ideas onto the page alongside fellow contributors. We also set monthly themes to spark fresh perspectives on the topics shaping our ecosystem.

We share everything, from sprint announcements to monthly themes, by email and through our WhatsApp community. Join us there to hear about new opportunities first.

Becoming a regular contributor

Some of our most-read voices started with a single article. Writing regularly helps you build a body of work that readers recognise, and the more you write, the more your profile and perspective grow with it.

If you’d like to make it a habit, you can also have your own column on e27. Your articles go out on a set day and time, weekly, fortnightly or monthly, so readers know when to expect you. To set one up, write to us at writers@e27.co and we’ll work out the details together.

Your next chapter

Here’s something most writing guides won’t admit: some of our strongest contributors would never call themselves writers. What they have is something worth sharing and the patience to show their working.

So write from what you know. Let AI carry the bags, not drive the car. Check your facts as if an editor is watching (one is). And keep enough of yourself in the draft that a reader could pick you out of a lineup.

Then hit submit. The worst that can happen is a few notes from us, and honestly, that’s where most good articles begin. When you’re ready for your next piece, we’ll be here to read it.

If you have a question at any stage, whether about your profile, your submission or anything in this guide, our FAQs cover most of the details. For anything else, write to us at writers@e27.co.

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Ready to share your perspective? Submit your article via the Contributor Programme here.

The post The e27 Contributor Programme: Using AI, submitting, and growing your readership — Part 2 appeared first on e27.

Hong Kong does not make many batteries. It has no lithium mines, no gigafactories, and industrial land prices that would make any cell manufacturer wince. Yet Hong Kong Science and Technology Parks Corporation (HKSTP) wants the city counted among Asia’s premier energy storage ecosystems, and it has now published a report saying so.

The inaugural Battery and Energy Storage (BES) Strategic Analysis Report, unveiled today at One Earth Summit 2026 by HKSTP CEO Terry Wong, is less a case for manufacturing muscle than for middleman value. It argues that Hong Kong’s strength lies in a four-stage value chain spanning research, systems integration, green finance and end-of-life battery management.

Also Read: Thirty climate tech startups in Southeast Asia that would rather build than pledge

It is a clever pitch. It is also a report written by the organisation that stands to benefit most from its conclusions, and it should be read that way.

The demand side is hard to argue with

The macro numbers are the least contentious part. According to the report, nearly 200 countries have pledged to expand renewables, with the goal of raising electricity’s share of global final energy demand from about 20 per cent today to 35 per cent by 2035. Electricity consumption from computing infrastructure is projected to more than double, from 415 terawatt-hours (TWh) in 2024 to 945 TWh by 2030. Investment in battery and storage technologies is expected to hit US$5 trillion (about HK$40 trillion) over the next decade.

That AI-driven power surge lands uncomfortably close to Southeast Asia. Johor, Batam and Greater Jakarta are absorbing hyperscale data centre builds faster than their grids can be cleaned up, a tension that threatens to blow a hole in ASEAN’s climate targets. Storage is the obvious bridge between intermittent solar and round-the-clock server farms, which is why Singapore has started putting money behind its own US$59 million grid fund.

The springboard pitch, and its limits

The report’s more strategic argument concerns China. Mainland firms account for more than half of the global battery applications market, and HKSTP positions Hong Kong as their preferred launchpad overseas. Three of the world’s top ten storage players, CATL, BYD and Gotion High-Tech, have “established a presence” in its ecosystem.

That phrase is doing a lot of work. The report does not say whether these are R&D centres, regional headquarters or registered entities, and the difference matters if the claim is about industrial depth.

The logic of the springboard also deserves scrutiny. As the US and Europe tighten rules on Chinese energy supply chains, HKSTP says mainland companies can lean on Hong Kong’s common law system, international certification and green finance platform to meet compliance requirements. Hong Kong can certainly help with paperwork, standards testing and capital. What it cannot do is change where a cell’s materials come from.

Washington stopped treating Hong Kong separately from mainland China for trade purposes in 2020, and Brussels’ battery rules trace carbon footprints and supply chain due diligence regardless of which city hosts the legal entity. Wong’s description of HKSTP as a “super-connector” is accurate; whether that connection survives the next round of trade tightening is another question.

Four stages, uneven weight

On research, the report points to five of the world’s top 100 universities and park companies such as Luquos Energy, a Chinese University of Hong Kong spin-off that claims to have built the world’s first sulphur-based flow battery. The company says it cuts costs and avoids the overheating fire risk of lithium-ion. Flow batteries are bulky and suited to stationary storage rather than vehicles, so this is a grid and building play, not an EV one.

Also Read: NEU Battery Materials bags US$7.6M to mine lithium from the world’s least-loved battery

Integration is where Hong Kong’s density becomes an asset. More than 60 per cent of the city’s private buildings are over 30 years old and face power capacity limits, making storage that charges off-peak and discharges at peak a practical retrofit. Park companies are also replacing diesel generators on construction sites, and Halo Energy has partnered with XPENG on what it calls the world’s first megawatt-level ultra-fast EV charging station in the city.

Finance is Hong Kong’s strongest card. The city is Asia’s largest arranger of green and sustainable debt, with related issuance topping US$76 billion in 2025. Considering how much of the US$37 billion promised to Southeast Asian climate projects has struggled to reach the ground, a finance-first hub has obvious appeal. HKSTP also signed an MoU this month with the Green Accelerator to link green tech startups with funding.

Recycling closes the loop. Hong Kong expects nearly 6,700 tonnes of retired EV batteries by 2030. Park company GRST has developed a non-toxic, water-soluble binder that it says cuts recycling-stage carbon emissions by up to 80 per cent, and the government opened the city’s first large-scale EV battery recycling facility last year. Southeast Asia faces the same wave sooner than it thinks: EVs are set to make up more than half of Singapore’s vehicle sales this year.

The neighbours are not standing still

Hong Kong is hardly the only city chasing this market. Shenzhen, a short train ride away, already has the factories. Singapore offers its own mix of green finance, rule of law and data centre demand. Indonesia has spent years using its nickel reserves to pull cell makers onshore, and Thailand has drawn Chinese carmakers, BYD among them, to build EV plants on its eastern seaboard. Vietnam’s VinFast, meanwhile, is betting on scale ahead of demand, with a 200,000-car plant for a 33,000-EV market in the Philippines.

Against that backdrop, Hong Kong’s edge is courts, capital and commercialisation rather than cells, the same final-mile-to-market argument it has made across other deep tech sectors.

The number to watch

HKSTP’s ecosystem currently has more than 250 green tech companies, around 100 of them in BES. It wants to lift that to over 160 in the next few years, a 60 per cent increase. The target is measurable, which is to its credit.

Also Read: Hong Kong builds a test track for robots that keep failing outside the lab

The harder test is whether the six companies HKSTP showcased at the summit, and those that follow, sell beyond the Greater Bay Area. Southeast Asia is the natural first market, yet Hong Kong founders have often looked north and west rather than south. If Hong Kong wants to be Asia’s battery broker, it will need to show up where Asia’s next grids are actually being built.

The post Hong Kong has no gigafactories, but it wants a cut of Asia’s US$5T battery boom appeared first on e27.

Indonesia has never lacked halal ambition. What it lacks is money willing to back that ambition.

Two weeks ago, at the 13th Indonesia Sharia Economic Festival (ISEF) in Jakarta, a coalition of regulators, investors and universities launched the Indonesia Halal Innovation and Venture Ecosystem (I-HIVE). The aim is to fix the second problem.

I-HIVE brings together 26 partners. They include Bank Indonesia, the National Committee for Islamic Economy and Finance (KNEKS), financial regulator OJK, planning agency Bappenas and five ministries, alongside investors, banks, industry associations and universities. The pitch is simple: give Indonesian startups building halal products a single route to local and global investors, make them strong at home, and then send them abroad.

Also Read: The VCs writing off Indonesia are making a US$300B mistake

The timing is deliberate. Startup funding in Indonesia fell to US$355.7 million across 91 deals in 2025, down from roughly US$6.9 billion at the 2021 peak, according to DiscoveryShift’s Indonesia Startup Report 2026. Economic think tank Celios estimates that only about 10 per cent of venture capital in the country’s digital economy comes from domestic sources.

That leaves founders dependent on foreign money that has largely gone elsewhere. The market is in what Tracxn described as a structural reset. Ajaib’s US$270 million raise in August was Indonesia’s biggest tech round since 2022. It was also the exception that proves the rule.

Demand is real, and now it is mandatory

The demand case is stronger than the funding case. The State of the Global Islamic Economy Report 2025/2026 puts Muslim consumer spending across six real-economy sectors at US$2.6 trillion in 2024 and projects US$3.56 trillion by 2029. The same report ranks Indonesia fourth globally. Bank Indonesia says the country’s halal value chain grew 5.5 per cent year on year in Q2 2026, faster than the wider economy.

Regulation adds urgency. The phase-in period for halal certification of food, beverages and slaughter products made by micro and small businesses ended on 17 October 2026. The scale is enormous. More than 9.6 million products had been certified by 2025, and the government planned 1.35 million free certificates for small businesses this year. That creates a large, slightly panicked customer base for compliance, traceability and supply-chain software. It is the kind of halal integrity problem technologists have pitched solutions for since before the pandemic.

The caveat is that the halal agency has said enforcement will begin with guidance and supervision rather than sweeping raids. Soft enforcement means softer urgency. And Indonesia’s MSMEs are big in number but weak in value. Millions of warung owners needing a certificate does not automatically translate into millions willing to pay for software.

What I-HIVE actually does

I-HIVE organises the ecosystem as a single pipeline. Startups move from venture formation through incubation, halal and investment readiness, and financing and market access, to scale. The pipeline connects eight groups of actors, from Sharia assurance bodies to Islamic social finance institutions.

Also Read: The KoinWorks era: What Indonesia’s SME lending journey has taught the next generation

I-HIVE will also run a sandbox for halal startups, in line with KNEKS’ roadmap, which targets acceleration in 2026 and 2027 and scale by 2030.

“What holds many back is access to investors who understand Sharia-compliant business and will stay with them as they grow,” said Helma Agustiawan, Deputy Director of Islamic Business Incubation at KNEKS.

What I-HIVE does not have, at least publicly, is a pooled fund or committed capital. It is a coordination layer. That is useful in a market where founders bounce between ministries, certifiers and investors. But 26 partners is also a recipe for a very long WhatsApp group. Coalitions of this size tend to succeed or fail on whether one or two members do the heavy lifting.

HASAN.VC wants to be that member

The most obvious candidate is founding collaborator HASAN.VC. Its Labuan-domiciled early-stage fund is backed by angels, family offices and waqf (Islamic endowment) funds across Southeast Asia, the Gulf and North America. The firm has built its thesis around ethical, halal investing and resilient “camel” startups rather than cash-burning unicorns.

It says it has backed 15 Indonesian startups over the past two years, which would make it among the country’s more active investors in a market with only 91 disclosed deals last year. The demo day it co-hosts with KNEKS shows both progress and scale.

In 2024, HASAN.VC was the sole investor behind Rp3.4 billion (about US$200,000) in deals. Deal value rose to Rp21.1 billion in 2025 and about Rp35 billion (around US$2 million) in 2026, a tenfold jump in two years. That figure spans both investment and trade, though, so not all of it is equity. And US$2 million is less than one per cent of a single Ajaib round.

HASAN.VC plans two more Indonesian investments before year end and a larger push in 2027. “What its startups need most is patient capital that shares the risk with them,” said General Partner Umar Munshi.

On stage, Munshi urged Indonesian investors to step up, arguing that local participation gives foreign investors confidence. It is the right message. It is also a hard sell in a year when prosecutions have rattled the venture community. Domestic investors have watched startup failure being treated as a potential crime, and that does not encourage risk-taking.

Gulf money and Dutch expertise

The cross-border corridors may matter more than the domestic pitch. Moria Ventures, a Jakarta-based Islamic investment firm, connects Saudi and Gulf investors with Indonesian companies. After several project-financing deals, it began venture investing six months ago and has made two investments. HASAN.VC has also invested in Moria Fund, Moria Ventures’ fintech business, in a corner of the market where Islamic fintech’s revival is still unproven. Partners backing partners is a sign of commitment. It also means the circle is still small.

“Saudi Arabia has the resources and the Vision 2030 mandate to deploy it,” said Moria Managing Partner Husni M. Bajamal. Gulf interest in Southeast Asia is rising across tourism and trade. But Indonesia competes for that money with Malaysia’s long-established Islamic finance ecosystem, and Gulf investors have historically preferred late-stage deals to seed cheques.

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

From Europe, PUM Netherlands brings 1,200 volunteer experts who advise SMEs in more than 30 countries. PUM was founded in 1978 by the Dutch Ministry of Foreign Affairs and employers’ federation VNO-NCW.

The real test

I-HIVE gets the diagnosis right. Indonesia’s halal founders sit on top of a growing, now partly mandated market, and they cannot raise money. A shared pipeline, a sandbox and investors who understand Sharia structures are all sensible fixes.

But the bottleneck is capital, not coordination. Next year’s ISEF will show whether I-HIVE has moved real money, and whether any of it came from Indonesian pockets.

The post HASAN.VC, Moria Ventures bet on Gulf capital to rescue Indonesia’s halal startups appeared first on e27.

Singapore’s data centres keep roughly 160 MWh of battery capacity on permanent standby, charged and waiting for a blackout that almost never comes. For an island that imports nearly all its fuel and is scrambling for clean power, that is a lot of expensive hardware doing nothing.

Amplicity, a Singapore-headquartered energy technology startup, wants to change that. The company has closed a US$1.01 million seed round backed by Loyal VC. It says it is in talks with more than 20 prospective customer sites across Asia Pacific and plans to enter Japan in 2027.

Also Read: SG Enviro closes Series A, targets water- and energy-hungry data centres and fabs in SEA

The pitch fits on a napkin: don’t build new batteries, make money from the ones already sitting in the basement.

The idle asset problem

Uninterruptible power supply (UPS) systems are the insurance policy of the data centre world. When the grid flickers, these batteries keep servers running for the minutes it takes diesel generators to start. The rest of the time, they sit idle.

This is where Amplicitly sees a massive opportunity. Its proprietary technology turns batteries into productive energy assets, from idle backup systems to operational battery energy storage and new installations. This way, data centres, industrial facilities and critical infrastructure operators can reduce electricity costs, generate recurring revenue and decarbonise, while preserving their backup requirements.

Amplicity estimates there is about 160 MWh of such capacity in Singapore’s data centres alone, and roughly 740 MWh across Asia Pacific, before counting factories and industrial sites. The company concedes that not all of it can be used flexibly. Its platform taps only the capacity above each site’s backup reserve. The operator sets the limits, and on-site controllers enforce them even if the internet connection drops.

That last detail is worth noting. The biggest objection from data centre operators, whose contracts are built around near-perfect uptime, is that anyone tinkering with the backup could cause the very outage it exists to prevent.

“The next energy storage opportunity is not just about building more batteries. We need to look at the resources we already have,” said co-founder and CEO Stéphane Leyo.

Where the money comes from

Most of the value, Amplicity says, comes from peak shaving. The batteries discharge during demand spikes, which is when large users pay a premium. Wholesale electricity market revenue is added on top.

The company’s own modelling suggests that a SGD500,000 (about US$388,000) battery at a Singapore data centre could deliver roughly SGD256,000 (US$198,000) in value a year. That is a striking return. On paper, the hardware would pay for itself in under two years. But it is a model, not a track record. Amplicity has not disclosed live deployments, megawatt-hours under management or signed customers. Being “engaged with” 20 sites is a pipeline, not a book of business.

There are technical questions too. UPS batteries were specified to sit fully charged, not to cycle every day. More frequent charging and discharging speeds up wear, and operators will want to know how that affects vendor warranties and replacement schedules. Amplicity’s Battery Value Assessment estimates what a site’s batteries could be worth before the operator commits. It will need to answer the wear question honestly to win over cautious facilities managers.

Also Read: Thirty climate tech startups in Southeast Asia that would rather build than pledge

Beyond existing backups, Amplicity also optimises operational battery energy storage systems. Where the economics justify it, it installs new ones with financing.

“By combining site-specific optimisation with electricity market opportunities, we can create economic value for businesses while supporting a more flexible energy system,” said co-founder and COO Gabriel Schiano.

Why the timing works

Timing is the startup’s strongest card. Data centres already consume about 7 per cent of Singapore’s electricity. The country has more than 1.6 GW of data centre capacity and plans at least 300 MW more, even as AI workloads push facilities to their power limits. About 95 per cent of its electricity still comes from natural gas, and clean power imports from its neighbours remain years away.

The state is already putting money into grid resilience. Distributed batteries at existing sites could help the grid absorb more intermittent renewables without new infrastructure. Loyal VC founding partner Michael Kosic called it “a capital-light way to support the energy transition.”

Crowded at home, tougher abroad

Amplicity is not alone in eyeing Singapore’s batteries. Blue Whale Energy raised US$2 million last year to build what it calls Singapore’s first battery-based virtual power plant. It deploys its own sodium-ion packs for commercial and industrial customers and holds an Energy Market Authority wholesale electricity licence. That is the opposite philosophy to Amplicity’s: one company owns the hardware, the other works someone else’s harder. The press release does not say whether Amplicity holds its own market licence or works through a partner.

Globally, power management giant Eaton has for years marketed grid-interactive UPS systems that let data centres sell services back to European grids. US-listed Stem uses software to optimise commercial storage fleets.

Amplicity’s edge is its model of working with existing equipment and requiring no new capital spending. That makes it easier to sell, because operators do not need a capital budget approved. The flip side is that the startup depends on clients trusting an outsider with their most mission-critical equipment. Japan, with its entrenched utilities and famously conservative procurement culture, will test that trust harder still.

Pedigree versus proof

Amplicity began at ENGIE Factory, the incubator of French energy group ENGIE, which was later acquired by Pacific Channel. It has been supported by SMU BIG, NUS Deep SAGE and Swinburne University’s Climate 4.0 Hub. It won the Pioneers in Sustainability Award at the France-Singapore Business Awards 2026 and is a finalist at this year’s AustCham Singapore Business Awards.

Also Read: Hong Kong has no gigafactories, but it wants a cut of Asia’s US$5T battery boom

Awards and incubator credentials open doors. Megawatt-hours under contract keep them open. Amplicity has a US$1 million cheque in a sector where climate tech capital is pooling in fewer hands, so it has little time to turn spreadsheet returns into batteries that earn.

The post Amplicity raises US$1M to put Singapore’s sleeping data centre batteries to work appeared first on e27.

Picture a leadership meeting that goes like this.

The air conditioner in the corner of the 22nd-floor boardroom had a faint, high-pitched metallic whine. Outside the glass, the heat off Robinson Road shimmered against the asphalt, but inside, the air was cold enough to raise goosebumps.

David, the CEO, leaned forward and rested his forearms on the table. Between his elbows sat three sheets of printed financial models, untouched.

“Marcus,” David said, his voice flat. “Who signed off on this?”

Marcus didn’t look up immediately. His right hand stayed glued to his mouse, fingers twitching as he clicked between browser tabs. On the 85-inch monitor on the wall, the dashboard was a sea of crimson. Unit economics had turned upside down in forty-eight hours. Customer acquisition costs in Jakarta had spiked 400 percent after an aggressive, offline pricing maneuver by an unlisted domestic incumbent that appeared nowhere in regional databases.

“We all did,” Marcus muttered, his eyes still fixed on the glass screen. “The model tested nine thousand market variables. It gave Pathway B an eighty-four percent confidence interval. We stress-tested the regulatory scenario. Priya’s inputs were in there. David, you ran the runway constraints yourself.”

David didn’t answer. He just looked at Marcus.

And that was the moment the tension in the room turned suffocating.

It wasn’t the panic of losing money. Founders lose money every day. It was the sickening realization of who was sitting across the table: six brilliant, highly compensated executives who suddenly looked like terrified schoolboys waiting for a hall monitor to tell them what to do.

Also Read: Muggles struggle, wizards juggle: What happens when everyone gets an AI familiar

Six months earlier, in that exact same room, they had toasted this decision with lukewarm coffee.

There had been no raised voices. No sharp words. When Priya hesitated on whether informal vendor networks in Indonesia would follow the synthetic pricing curves, Marcus hadn’t leaned into her doubt to explore it. Instead, he simply tapped his keys: “I just ran Priya’s hesitation through the reasoning model. It discounted our baseline by twelve percent and Pathway B still beats Pathway A on every capital efficiency metric.”

Priya had swallowed her objection. The tension had vanished before it could breathe. The room had let out a collective, relieved exhale. The friction of having to look a peer in the eye and say, “I don’t care what the math says, my gut tells me this will get us killed” had been neatly, scientifically eradicated.

They thought they had achieved friction-free clarity.

Now, staring at the wreckage, the brutal reveal hit everyone in the room: They hadn’t bought strategic certainty. They had bought an organisational alibi.

The system had not deceived them. The algorithm had done precisely what it was designed to do: calculate probability based on historic distributions and low-variance precedent. The betrayal had not come from the silicon; it had come from the humans. They had subconsciously used the machine’s polished defensibility to excuse themselves from the agony of genuine courage.

Nobody had fought for Pathway B. Nobody had sworn an oath to it. They had simply taken refuge inside a consensus so mathematically tidy that if everything went to hell, nobody’s neck would be on the chopping block.

Behavioural scientists call this automation bias and accountability diffusion—the quiet sedative that makes smart leaders surrender their skeptical edge the moment an automated system presents an elegant narrative. It is what researchers from Harvard, MIT, and Wharton documented when studying elite professionals using generative models: when the problem reaches the edge of uncharted terrain, humans fall asleep at the wheel.

And as a sweeping meta-analysis across over one hundred experiments published in the Academy of Management Perspectives proved, human-AI teams routinely fail to outperform individuals working alone—not because the technology lacks power, but because humans abandon the friction required to challenge it.

When AI mediates strategic consensus, governance dies. As Harvard Business Review points out, consensus models collapse under the weight of AI synthesis. The moment responsibility is spread across a prompt and an algorithm, everyone is an observer, and no one is an owner.

Also Read: How we use AI to triage inbound B2B leads without blasting everyone the same way

Every competitor in your market has access to the exact same reasoning tools and data pipelines you do. If your executive sessions only exist to rubber-stamp the lowest-variance algorithmic path, your team will drown in polite, defensible mediocrity.

If you lead a company today, you must erect three non-negotiable boundaries:

• First, eliminate composite consensus. An algorithm can sketch the battlefield, but it cannot take the hill. Before signing off on any high-stakes bet, outlaw shared ownership. Force one executive to step out from behind the group, take the pen, and sign their name alone on the charter. If the plan ruptures, they are forbidden from citing the prompt or the confidence interval. Authorship must carry a pulse.
• Second, reintroduce the friction by turning the system into an enemy. When your executive offsite reaches effortless alignment on an AI-synthesised roadmap in under an hour, refuse to adjourn. Feed the plan back into the model with a direct command: “Act as our most aggressive competitor and our most cynical regulator. Tell us why this plan is intellectually cowardly, and highlight the assumptions we are accepting solely because they spare us from arguing.” Force the real human tension back into the room.

Most of all, understand the sovereign threshold. A neural net can model your burn rate, project user churn, and draft emergency scenarios. But it will never look an engineer in the face on a Friday afternoon when payroll is missed and say, “This was my decision, and I will fix it.”

True leadership does not exist within the parameters of the training data. It begins at the exact moment the data runs out.

The next time your leadership team gathers around a conference table with an airtight, AI-assisted proposal, don’t look at the slides. Look at the person championing the initiative.

Look them in the eye and ask: If this breaks tomorrow, and every algorithmic model is revealed to be dead wrong—are you prepared to stand in front of this company and take full, solitary custody of the wreckage?

If their hands shake, or if their eyes dart toward their screen before they answer, shut down the meeting. You do not have a strategy. You just have a very expensive excuse.

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The post The sovereign threshold: Why airtight AI decisions are paralysing executive teams appeared first on e27.