Most conferences are built the same way. A committee brainstorms a theme, a team chases speakers, a designer lays out a program, and everyone hopes the real value happens in the corridors. Three days in Dubai this October worked differently.

The operating model was deliberately lean: two people shaping the ideas and running the room, and two part-time organisers steering the AI that handled the rest. Phil Midland and Dr Alex Lin of Strategic Renaissance 21 (SR21) designed and facilitated the workshop with the World Government Summit, while Khor Zijian and Lin Fanghong piloted the AI.

The result was not a faster version of a conventional conference. It was a different kind of event.

Finding the theme: “The Breaking Point”

The theme was derived, not brainstormed. Before naming a single session, AI worked through a large body of material: think-tank reports, academic papers, policy articles, market commentary, and news analysis on geopolitics, energy, capital flows, financial trust, and artificial intelligence. The question was what the world’s strategic conversation was converging on.

The signal was consistent. Across domains that rarely share a stage, analysts were describing systems under cumulative stress: supply corridors, debt and reserve-currency dynamics, trust in banks and institutions, and the speed at which AI was entering all of them. The shared question became the event’s framing: how much stress can the complex world system absorb before it breaks, and what does rebuilding look like?

AI surfaced the pattern, and the humans decided it was worth building a conference around. That division of labor held throughout.

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

Sessions built around the people in the room

A good theme is useless if the room can’t speak to it. The second job for AI was turning “The Breaking Point” into sub-themes that matched what the confirmed participants actually knew.

The system took each attendee’s profile, publications, and professional background and mapped them against candidate angles. The program settled into four tracks:

• Track A: kinetic conflict and hard power
• Track B: capital and energy
• Track C: trust, in banking and human systems
• Track D: AI as multiplier and disruptor

The regional focus was the Middle East, North Africa, and the Indian Ocean. Each day moved from situational awareness to rebuilding. As the attendee list shifted, AI re-tested the track assignments against who was actually coming. Matching speakers to sessions by hand is slow and tends to default to reputation. Matching against demonstrated expertise produced sharper sessions.

It also fit the kind of room SR21 convenes. As SR21 Chairman Dr. Howard Morgan said:

“Every year this group gets a little better at seeing structural change before it becomes conventional wisdom. This year we’re pushing that a step further — turning what we see into commitments the room can actually be held to.”

We built the program to give that ambition somewhere to go.

Preparing the room before the room existed

The usual weakness of a high-level gathering is that participants arrive cold and spend the first day getting oriented. We remove that.

For each track, AI assembled a reference reading list, then synthesised it into a concise thematic paper and a podcast-style audio briefing. Participants could read at their desks or listen on the way to the airport. The goal was a shared baseline of facts and framings, so that conversation could start the minute the group convened.

Each paper also included proposed discussion points and prompts for facilitators. These were designed to cover every angle of a topic, including the uncomfortable ones, and to draw out insights from both the speakers and the audience. A quiet expert in the third row is often holding the most useful observation in the room. A well-built prompt gives the facilitator a reason to go and get it.

The papers were also built backward from the final day’s goal, which I’ll come to shortly. Each track’s reading mapped to the practical measures participants would draft on day three, so the preparation fed directly into the output.

The logistics: The least significant AI contribution

It’s worth being honest about this part, because most people assume AI’s value lies here. AI did the manual compilation: assembling the pre-conference materials into a single conference booklet, going through multiple versions as the program, venue, and schedule changed, and drafting press releases with quotes from relevant personalities.

This saved real hours, and it let two part-time organisers keep up with a moving target. But it was probably the least significant thing AI did. Formatting a booklet is a task any competent assistant could do. The real value was upstream, in how the content was chosen and prepared, and downstream, in what was extracted from the proceedings.

Also Read: How AI became my sparring partner

In the room: Recording everything, analysing beyond the words

From 5 to 7 October at Emirates Towers, the proceedings were recorded for immediate analysis. The workshop brought together 48 senior leaders, including retired flag officers, sitting and former government ministers, sovereign-wealth and private-capital principals, technologists, and academics, all under the Chatham House Rule. Ministers from the UAE government attended, including H.E. Omar Sultan Al Olama, Minister of State for Artificial Intelligence, Digital Economy, and Remote Work Applications, and his deputy.

Conventional session summaries record who said what. Ours were built to capture what the conversation meant, and AI let us go well past the spoken word:

• Bias and opinion identification. We separated statements of fact from statements of position, and flagged the framing behind each.
• Data error citation. When a speaker quoted a figure that didn’t match the published record, the system noted the discrepancy and cited the correct source, rather than letting it pass into the summary as truth.
• Emotional and persuasive language. AI flagged loaded or persuasive wording, showing where the heat in a discussion was and where conviction outran evidence.
• External trajectory data. We cross-referenced on-stage discussions with prediction markets like Polymarket and Google Trends to see where public attention and market probabilities were heading. A topic was no longer just “discussed”. It came with a trajectory.

No human team could do this at the speed a conference demands. A person can listen to a panel, but cannot simultaneously fact-check every figure, score the rhetoric, pull live market data on each topic raised, and produce a structured summary within hours. The AI did the heavy lifting, and the participants got insights they couldn’t have extracted themselves.

Humans stayed where they add the most value: choosing which questions mattered, judging which signals were real, and deciding what the findings meant. AI extracted. People decided.

Day three: From diagnosis to Trust and Confidence Building Measures

A conference that only diagnoses a problem leaves people anxious and no better equipped. So the final day focused on practical Trust and Confidence Building Measures (TCBMs), concrete mechanisms to help the world be better prepared before it reaches the breaking point.

The drafting session drew on the tracks’ reference readings and insights from the previous two days. Because the workshop ran under the Chatham House Rule, I won’t detail the individual measures. In general terms, they covered AI system governance, the security of critical trade corridors, human oversight of automated decision-making, verification of de-escalation steps, and the interplay of energy, capital, and currency stability.

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

Because the earlier work was systematically prepared and captured, participants didn’t start day three from scratch. The system also summarised the discussion into actionable insights and draft plans for moving forward. As a result, local and international participants alike left with drafted language, shared frameworks, and a clearer sense of who to talk to next. They left with tools and ideas, and were better prepared for what’s next.

What this means for event organisers

For Asia’s startup and venture community, which attends and runs more conferences than almost anyone, a few lessons stand out:

• Use AI upstream, not just at the admin layer. Theme derivation and session design are where it changes outcomes. Booklet assembly is a convenience.
• Prepare participants as seriously as you program speakers. Synthesised readings, audio briefings, and facilitator prompts raise the floor of every conversation.
• Treat the recording as raw material. An event’s most valuable output is often the analysis of what was said, not the footage.
• Keep humans on judgment. The team’s small size was a feature. Two part-timers could pilot the AI, and two facilitators could run the room, because the AI handled breadth.

What comes next

The engine behind the in-room analysis is a process we call Machine Augmented Insights Extraction (MAIE). In my next article, I’ll walk through the detailed, AI-assisted flow of MAIE: how a raw conversation becomes a structured, fact-checked, trajectory-aware set of insights, and how you can apply it to your own meetings, boards and events.

The Dubai workshop made one thing clear to me. A conference is no longer just a meetup and a networking event. It can be a deeper learning experience, one that gives participants far more than a presentation on stage can.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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Bitcoin slipped 0.97 per cent to US$79,912.85 over the last 24 hours. That number alone looks mild for an asset known for violent swings. The total crypto market cap fell 0.72 per cent to US$2.68 trillion. Bitcoin’s seven-day correlation with the S&P 500 reached 83.4 per cent. That correlation explains the move better than the price change. This was not a crypto-specific breakdown. It was a risk-asset repricing shaped by fragile positioning and tougher financial conditions.

Institutional flows supplied the first push. US spot Bitcoin ETFs lost about US$635.5 million on October 22 and another US$137.9 million on October 23. Combined outflows reached roughly US$773.5 million over two days. That reversal followed prior-week inflows. The speed of the turn challenges the idea that ETF money is steady and structural. It behaves tactically. It chases momentum and exits when fear rises. This flow reversal added direct selling into an already nervous tape.

Leverage amplified the decline. Leveraged positions fell roughly US$815 million to US$837 million in 24 hours. Bullish traders took most of the damage. Total derivatives open interest fell 8.6 per cent, which confirms broad deleveraging rather than an isolated spot sale. Stop losses and forced sales fed the drop. The flush removes speculative weight, but it also shows how crowded longs had become. A market cannot build a durable rally on a foundation of stretched positioning.

Geopolitical risk added another layer. Weekend headlines reported that President Trump said the US could strike Iran as soon as Sunday. Bitcoin trades around the clock and absorbed early selling before traditional equity markets opened. This is a structural disadvantage in a crisis. Investors seeking protection do not treat Bitcoin as a hedge. It trades like a risk asset. The 83.4 per cent correlation with the S&P 500 reinforces that role. Short-term behaviour is currently indistinguishable from equities, and traditional equity futures were also weak.

Also Read: Bitcoin rejected US$84,000 and forced US$79,060,000 in long positions out

The rates backdrop made the selling easier. The 10-year Treasury yield remains elevated near 5.3 per cent, and September CPI came in at 3.7 per cent year over year. Sticky inflation and high yields reduce demand for non-yielding volatile assets. Crypto sits at the far end of the risk curve, so it feels that strain more acutely than most sectors. The Federal Reserve’s October 28 decision is the key near-term catalyst. A hawkish tone would extend the pressure and make a test of US$77,600 more likely. A dovish shift, or cooling inflation data on October 29, could stabilise prices and give the asset class room to breathe. Traders are not waiting for a crypto-native catalyst. They are waiting for an economic signal.

The technical picture tells a similar story. Bitcoin trades below the US$80,000 to US$81,500 resistance zone, which aligns with the estimated cost basis for ETF and corporate treasury holders. A sustained failure to reclaim that zone keeps the bias bearish and puts US$77,600 in play, a level Glassnode identifies as the True Market Mean. On the total market cap chart, US$2.68 trillion is the immediate pivot. A hold above that level could open a rebound toward US$2.74 trillion. A break below risks testing the US$2.6 trillion to US$2.64 trillion range. These are not arbitrary lines. They are levels where holders have shown conviction before, and where the next battle will take place.

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

My point of view is that this moment is a healthy but uncomfortable reset. It exposes two weaknesses that the bullish narrative prefers to ignore. The first is that institutional demand through ETFs is far more fragile than the adoption story admits. The second is that leverage remains a permanent feature of this asset class, and it turns ordinary corrections into cascade events. Neither weakness is fatal. Both argue for humility about how quickly Bitcoin can decouple from the broader financial system. The correlation with the S&P 500 is not a temporary anomaly. It is the current regime.

The near-term outlook is bearish below US$80,000, but that bearishness is conditional. The Fed could change the mood in a single statement. The daily close relative to US$80,000 and the policy language on October 28 will decide whether this is a brief shakeout or the start of a deeper move toward US$77,600. My view is that the next leg will come from policymakers, not crypto developers, ETF issuers, or on-chain metrics. Bitcoin’s short-term fate is not in its own hands. This sell-off reveals that uncomfortable truth with unusual clarity.

—

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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When a chip plant is running close to full tilt, a failing chiller or a slow gas leak is not a maintenance ticket. It is lost output that cannot easily be made up elsewhere.

That is the problem GlobalFoundries (GF) says it is tackling in Singapore. The New York-headquartered contract chipmaker has opened an Advanced Facilities Control Centre (AFCC) at its Singapore site, developed with German industrial giant Siemens.

Also Read: The quiet layer keeping the chip boom alive

In a statement on Monday, GF said the centre is the first deployment of a system meant to integrate facilities operations and data across its global manufacturing network. Singapore is the first site to get it.

What the control room actually does

In a fab, “facilities” means the life-support systems around the production tools: power, cooling, ultrapure water, specialty gases and the filtered air that keeps cleanrooms clean. If any of them falters, the wafers inside are at risk.

In the first phase, five fab-specific control centres across GlobalFoundries’s Singapore campus will operate as a single command centre. The system pulls facilities data and operational metrics into one view, using monitoring and visualisation tools and AI features such as predictive maintenance, which flags equipment likely to fail before it does.

GF says this helps teams troubleshoot faster, find root causes sooner and decide which problems to fix first. Once fully deployed, the system can integrate more than one million critical data points from facilities equipment. Round-the-clock equipment health monitoring is meant to cut unplanned downtime, and metrics can be compared across sites to improve performance and sustainability.

The centre also gets mobile eyes. Autonomous robots carrying thermal imaging cameras, gas detectors and vibration and acoustic sensors will patrol the fab floor and campus, streaming real-time data back for analysis.

Why Singapore, and why now

Singapore is no side project for GlobalFoundries. The company opened a US$4 billion expansion fab there in September 2023, lifting its local capacity to about 1.5 million 300mm wafers a year. In November 2025, it acquired local silicon photonics foundry Advanced Micro Foundry (AMF). Silicon photonics uses light rather than electrical signals to move data between chips, a technology AI data centres increasingly need.

Timing matters here. DIGITIMES reported last month that GlobalFoundries’s Singapore capacity utilisation had moved above 90 per cent, and that equipment lead times were constraining its photonics expansion. When new tools take months to arrive, squeezing more uptime out of existing plant becomes the cheapest capacity a chipmaker can buy. It fits the wider pattern of AI’s bottleneck economy shifting from GPUs to the less glamorous parts of the supply chain.

Also Read: Applied’s US$500M Singapore expansion tightens ties with Southeast Asia’s chip push

Yew Kong Tan, GF’s senior vice president and general manager for Asia-Pacific manufacturing and the Singapore site, said the city-state was the natural starting point because of its digital foundation and government and ecosystem support. Cindy Koh, executive vice president of the Singapore Economic Development Board (EDB), framed the centre as an example of Singapore’s collaborative approach to manufacturing, particularly in advanced packaging and silicon photonics.

Those are precisely the niches Singapore has been branding hard. Last month, A*STAR and EDB launched SG Semiconductor as a national identity for the sector. Chipmakers across the region are also expanding their Asia Pacific footprint to meet AI demand.

The numbers the announcement leaves out

For all the talk of one million data points, GlobalFoundries’s statement contains few numbers that matter to an outsider. It gives no target for reduced downtime, no estimate of energy or emissions savings and no figure for what the centre cost.

The workforce question is also handled delicately. GF says centralisation will optimise the use of “space, equipment and staff” and cut duplicated infrastructure. Tan said the tools would free facilities engineers to focus on higher-value diagnostics and problem-solving. That may well be true. But when five control rooms become one, fewer people usually end up watching the screens, and GlobalFoundries has not said what happens to the rest.

There is a security trade-off too. Folding a campus’s critical infrastructure data and robot feeds into a single command centre makes operations easier to see. It also gives attackers one high-value target. Chip suppliers in the region are already under rising pressure to prove cyber resilience before customers will sign.

Then there is sustainability. Fabs are notoriously water- and energy-hungry, so lower energy use and emissions is a welcome promise. Without a baseline or a target, it is hard to hold GF to it.

Pradip Singh, GF’s chief manufacturing officer, called reliable facilities infrastructure foundational to chipmaking and described the centre as a step towards a more connected, intelligent manufacturing network.

A crowded race to run the smart fab

GlobalFoundries is hardly alone. TSMC has long promoted AI-driven “intelligent manufacturing” at its fabs. In Singapore itself, UMC runs its Fab12i and VisionPower Semiconductor Manufacturing Company, the Vanguard-NXP joint venture, is building a new plant. Each will be chasing the same uptime gains.

On the software side, the deal is a showcase for Siemens in a market where rivals are spending heavily. Schneider Electric agreed earlier this month to acquire industrial software firm PTC for about US$22.6 billion, and players such as Honeywell and ABB also sell plant monitoring and automation. A template that GF rolls out across its network would make Siemens a reference supplier for a global foundry.

There is room for smaller players too. Startups selling vision AI to reduce downtime or sensor analytics for industrial plants will see a big chipmaker validating the category.

What to watch

Singapore’s pitch to chipmakers was never cheap land or cheap labour. It is being the place where the next way of running a fab gets tried first. If the AFCC works, the model will travel to GF’s sites in New York, Vermont and Dresden, and Singapore will have exported a blueprint rather than just wafers.

Also Read: Synopsys, A*STAR team up to tackle AI chip packaging challenges

The real test comes in a year. If GF can then publish hard figures on downtime avoided and energy saved, the control room will have earned its billing. If it cannot, the robots patrolling the fab floor will look more like a photo opportunity than a productivity story.

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Singapore has a power problem that no amount of rooftop solar will fix. The city-state generates about 95 per cent of its electricity from imported natural gas. It has little spare land for solar farms, no rivers for hydropower and not much wind. Every decarbonisation plan it writes is, in effect, a plan to import cleaner molecules or squeeze more energy out of very little space.

The latest instalment arrived on Monday. The Energy Market Authority (EMA) and the Advanced Energy and Decarbonisation Office (AEDO) launched a SGD75 million (US$59 million) Power Decarbonisation Funding Initiative to develop low-carbon technologies for the grid. It sits under the SGD800 million (US$627 million) Decarbonisation Grand Challenge, part of the Research, Innovation and Enterprise (RIE) 2030 plan announced in March.

Also Read: AI is about to blow a hole in ASEAN’s climate targets, unless baseload goes clean

The money will fund laboratory research on early-stage technologies as well as test-bedding and pilots of more mature ones. It targets three areas: hydrogen and its derivatives, solar and biomass.

Hydrogen gets the hardest targets

Hydrogen carries the most demanding goals. The first is to let power plants switch from hydrogen-based fuels to backup fuels within 10 minutes while keeping combustion stable. A turbine burning hydrogen or ammonia cannot simply sputter when a shipment runs late. Grid operators need a fallback that kicks in before the lights go out.

The second is to shrink the health and safety buffer required around hydrogen facilities with a capacity of 1 million tonnes per annum of hydrogen equivalent. On an island where industrial land is rationed by the hectare, a smaller exclusion zone is a direct saving.

The third goal is the boldest. The initiative wants to bring the cost of extracting hydrogen to below a dollar per kilogram, through better energy efficiency, longer-lasting catalysts and revenue from valuable co-products. Singapore expects to import hydrogen in carrier forms such as ammonia rather than produce it at home. That makes the “cracking” step, which releases hydrogen from its carrier, the place where costs pile up.

The hardware is already arriving. Last Friday, Singapore broke ground on a SGD62.5 million (US$49 million) low-carbon testbed on Jurong Island, where Amogy will pilot ammonia cracking at up to one tonne of hydrogen a day. Whether cracking can ever get under a dollar a kilogram at commercial scale remains one of the energy transition’s open questions.

Solar on walls, not fields

With most rooftops and reservoirs spoken for, the solar targets focus on unconventional surfaces. The initiative wants structural costs cut by at least 15 per cent without compromising strength or waterproofing. This is the unglamorous engineering that decides whether panels end up on facades, sea walls and car parks, or stay in brochures.

It is also chasing next-generation photovoltaic modules with efficiency above 30 per cent, or lightweight and flexible designs. Either kind must survive Singapore’s heat and humidity with annual degradation below 1 per cent over at least 25 years.

Also Read: Touchstone partially exits solar startup Stride after US$15M Series B

That is a tall order. Mainstream commercial silicon panels convert roughly 22 to 24 per cent of sunlight. Anything above 30 per cent today generally means tandem cells, which layer silicon with materials such as perovskite. Those cells remain largely lab-scale and are notoriously sensitive to moisture. Asking them to last a quarter of a century in the tropics is asking a lot, which is presumably why the government is paying for research rather than procurement.

The ash nobody wants

The biomass goal is narrow but practical. The initiative wants low-cost technologies that turn ash from different biomass feedstocks into useful products, at a processing cost below SGD50 (US$39) per tonne. Burning wood pellets or agricultural waste leaves residue. Singapore’s only landfill, Semakau, is projected to fill up by around 2035, so the country has little room to dump it.

Regional startups are already working on adjacent problems. Singapore’s Verta Bioenergy turns farm waste into coal-ready fuel for industrial boilers. Some investors argue that the region’s next climate unicorn may be built from agricultural residue. Ash is the less glamorous end of that value chain, but someone has to deal with it.

Read the DRIVE fine print

For founders, the most relevant part is the Decarbonisation Research for Industry, Ventures, and Enterprise (DRIVE) programme, which opened for applications on 23 October. SGD25 million (US$20 million) of the power initiative is reserved for industry-designed projects.

DRIVE flips the usual grant logic. Companies, including startups and technology providers, define the decarbonisation problem they want solved. They then work with Singapore’s public universities and research institutions to develop and co-fund solutions. The government covers up to 75 per cent of qualifying project costs, but that money goes to the public research performers, not to the company. The company puts in its own share.

That makes DRIVE less a startup grant than a subsidised research contract. For a well-capitalised corporate, it is a cheap way to rent a university lab. For a seed-stage climate founder, funding even a quarter of a multi-year research budget is a stretch. Climate capital promised to the region has been slow to actually arrive, and among Southeast Asia’s climate startups the cheques remain stubbornly small.

Hardware founders face the steepest climb. Climate finance has long struggled to back companies building in atoms rather than code. Those are precisely the companies a hydrogen-cracking or solar-materials challenge would attract.

A crowded week of announcements

The power initiative did not arrive alone. In the space of four days, Singapore also announced:

• a SGD143 million (US$112 million) industrial decarbonisation fund
• the Jurong Island testbed
• a renewed energy cooperation pact with Malaysia covering cross-border electricity trading and renewable energy certificates

The overlap can be hard to track. The Economic Development Board says up to S$100 million of the industrial fund is also earmarked for DRIVE. DRIVE therefore looks like an umbrella programme with separate pots for power and industry. Founders will want clarity on which pot their project falls into before they write a proposal.

Also Read: Funded: SEA climate tech has US$1.1B and a problem no one wants to name

None of the targets comes with a deadline, and no baseline has been published against which progress can be measured. A sub-dollar hydrogen cost or a 30 per cent tropical solar panel is a worthy ambition. Without dates, it is difficult to hold anyone to it.

There is also a question of proportion. SGD75 million spread across turbine controls, safety engineering, catalyst chemistry, solar materials and ash recycling is a research budget, not an industrial policy. Singapore’s real decarbonisation bets remain imported electricity and imported hydrogen. Home-grown research buys option value, a hedge in case those imports prove slower or costlier than planned.

For startups, the opportunity is real but conditional. Those with a corporate partner, a clear technical problem and a quarter of the bill in hand will find DRIVE useful. Programmes such as Startupbootcamp’s Singapore sustainability cohort suggest the founder pipeline exists. Everyone else may find that Singapore has built a well-equipped lab, with an entry fee attached.

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The most telling line in Publicis’ announcement of a new AI Development Hub in Singapore isn’t about technology. It’s the promise that the hub will create roles rather than remove them. The hub itself is a serious bet: engineers, data scientists and product designers working alongside strategists to build proprietary marketing technology in Singapore and scale it outward. But the reflex behind the promise is the oldest one in the agency business. When in doubt, hire.

That reflex explains why the biggest groups overbuilt headcount for a decade while the economics were asking them to build product.

Headcount was the product

Agencies sell capacity. Revenue follows people, margin is measured as EBITDA, and the metric rewards a busy bench, so hiring ahead of the next pitch is not a lapse inside that model. It is the model.

The bill arrives when clients stop paying for capacity. Investing.com’s snapshot of the sector puts digital ad spend growth at about 8.7 per cent in 2025 against agency revenue growth near zero, as clients protected media budgets and squeezed fees, and shows Omnicom heading from roughly 120,000 staff to about 105,000 by year-end. Per the Financial Times, relayed by Reuters, WPP cut about 11,000 jobs since the start of 2025, leaving 97,388 people by June.

What the market pays for it

Public agency groups traded at a median 0.8x revenue and 6.6x EBITDA at the end of 2025, according to Canaccord’s data as compiled by Axia. Independent agencies typically sell for 3-7x EBITDA, with strategic outliers reaching 8-12x. Martech M&A tells a different story: a median of 3.5x revenue in 2026 so far, and 8.0x for AI-native companies.

Line up the revenue multiples and a dollar of martech revenue is priced at more than four times a dollar of agency revenue, and AI-native martech at about ten times. Different samples, so read it as direction rather than arithmetic.

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

The top of the ladder sits higher still. ValueAdd VC’s October tracker puts the median multiple for nine private AI companies at 27.1x annualised revenue, against 4.3x for public SaaS. In marketing, Clay raised at a US$7.1B valuation in September on roughly US$200M of run-rate revenue, about 35x, while non-AI martech such as Seismic and AppsFlyer sit near 12x and 8x on Multiples.vc. Private marks on annualised figures are soft, but the pattern holds: the premium follows AI-native revenue, not the martech label.

The sharpest illustration sits inside the holding companies. WPP’s 14.8x EV/EBITDA is the highest of the big three, yet it is a symptom, not a premium: its EBITDA fell from US$1.96 billion in 2021 to US$752 million in FY2025, so the denominator collapsed. Publicis, the one still growing, sits at 9.6x. A model measured on EBITDA gets punished twice when it shrinks.

Two people, one payroll, opposite incentives

An agency owner is paid on EBITDA and is a capacity manager by training: utilisation, a full bench, a retainer renewed. A buyer then discounts that earnings stream for founder dependency and client concentration, which is why most independents land well below public-market multiples.

A martech CEO is paid on a different question: how much more revenue can this customer base produce without adding cost? Net revenue retention, gross margin and growth set the price. Public software with retention cohorts above 120 per cent traded at 11.7x revenue against 4.1x below 100 per cent, in SEG data compiled by Array Capital. Levels have compressed since; the ratio is the point.

So the owner reads a new hire as growth. The CEO reads it as a cost to be spread across more customers. Same payroll, opposite incentive. That gap, more than any technology gap, is why transformation stalls: moving to a product model means treating your own headcount as the thing to decouple from revenue.

What would make an agency price like product

Publicis shows the discipline is possible. It keeps headcount discipline as a rule, holding headcount growth below organic revenue growth, with about 114,000 people and roughly 5,100 net hires in 2025. WPP put more than £300 million into its WPP Open platform in 2025 while shrinking staff. Those are product bets funded by payroll cuts, which is not the same as a product business.

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

Three tests separate the two:

• Revenue that doesn’t rise with hours: licences, usage or outcome fees.
• Retention you can show cohort by cohort, not client anecdotes.
• A margin story that survives automation, with headcount growing slower than revenue.

Whether the Singapore hub passes depends on whether its output is licensed as software or absorbed into fees. A lab that makes the service faster still sells time; software others can license opens a different line on the P&L.

Why Southeast Asia feels it first

A joint survey by Singapore’s Economic Development Board and McKinsey, cited in the Publicis announcement, found 73 per cent of Southeast Asian companies piloting or scaling AI against 57 per cent globally. f clients are moving that fast, agencies still pricing by the hour risk falling behind the businesses they serve.

A 50-person independent can’t fund an R&D hub and shouldn’t try. It can stop selling hours where it doesn’t have to, and the research and pitch layers are the first place to start.

The question underneath

Among the independent agencies I work with, I’ve seen research and pitch work move from payroll to a repeatable product. One 50-person Singapore agency cut brand analysis from 10 hours to one and client-reporting costs from US$10,000 to US$6,000 a month. Another went from a three-person proposal team to one and cut cost per proposal by 30 per cent.

Owners who want a product multiple have to be seen thinking like product companies, which starts with a steady voice about what they’ve automated in their own shop and what it returned.

So here is the question worth sitting with: if you stopped hiring for twelve months, how much of your revenue would still grow?

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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.

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Grab wanted Taiwan to be its first market outside Southeast Asia. Taiwan’s competition regulator has told it, in effect, that it is not really an outsider.

Taiwan’s Fair Trade Commission (TFTC) on Wednesday blocked Germany-based Delivery Hero’s planned sale of foodpanda’s Taiwan business to Grab, scuttling a US$600 million all-cash deal agreed on March 23. The commission said it had discussed possible remedies with the parties, but the commitments on offer were not enough to offset the damage to competition.

Also Read: Grab’s US$1.49B Atome deal signals a deeper race for SEA’s credit economy

Delivery Hero said the decision does not affect its right to appeal. Grab said it was disappointed, arguing the ruling stops it from bringing new services, better earning opportunities and improved service standards to Taiwanese consumers, couriers and merchants. It added, a little pointedly, that its core business across Southeast Asia “remains robust”.

This is the second time in under two years that Taipei has stopped foodpanda Taiwan from changing hands. In December 2024, the regulator blocked Uber’s roughly US$950 million bid for the same business because the combined entity would have controlled more than 90 per cent of the market. Grab’s bid was meant to be the antidote: an entrant with no Taiwanese operations, buying foodpanda to keep the island’s two-horse race alive. When the deal was announced, e27 argued it could save Taiwan from a delivery monopoly. The TFTC has concluded that the two horses would share a stable owner.

Uber in the room

That owner is Uber. The US company holds about 13.1 per cent of Grab, a legacy of the 2018 deal in which Grab swallowed Uber’s Southeast Asian business, and an Uber executive previously sat on Grab’s board. Grab’s dual-class share structure caps Uber’s voting power at 3.9 per cent, but the commission was unmoved.

Its reasoning runs on four tracks:

1. Uber would pocket a slice of Grab’s profits, which blunts Uber Eats’s incentive to undercut a Grab-owned foodpanda with cheaper fees or promotions.
2. The shareholding changes Grab’s own incentives, voting caps notwithstanding: it is hard to wage a price war on a company that owns a chunk of you.
3. The two companies have worked closely together in other markets, which the TFTC said makes coordination easier to predict and sustain.
4. Taiwan’s delivery market is difficult to break into, because a newcomer needs deep pockets and enough restaurants, couriers and customers all at once, leaving little room for anyone to punish a comfortable duopoly.

The third point is the sharpest. The commission singled out the 2018 Grab-Uber transaction, saying both parties knew or should have known it broke competition law. That is not a speculative charge. Singapore’s competition watchdog fined the two companies a combined S$13 million (about US$9.5 million) over that merger. Taipei has effectively entered that history into evidence, and it said Uber would need to cut its Grab stake further, or exit altogether, to ease the risks.

The Delivery Hero twist

There is also a complication the four points do not spell out. In July, Uber agreed to buy Delivery Hero itself in a deal valuing the German group at about US$14.8 billion, on top of the roughly 25 per cent stake it already held. Taiwan was carved out of that transaction, but the TFTC had already asked Uber whether owning Delivery Hero could still give it influence over the foodpanda sale.

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

Strip it down and the picture is awkward. The seller is about to become Uber’s subsidiary. The buyer counts Uber as a major shareholder. The incumbent rival is Uber Eats. A regulator that had already said no to Uber once was never going to be relaxed about a deal in which Uber sat, in some form, on every side of the table.

Local opposition did not help. A lawmaker and delivery workers urged the commission to reject the deal outright, calling it a “de facto monopoly”. The National Delivery Union cited a government-funded Institute for Information Industry study that ranked Grab last among five potential buyers. Critics also flagged Grab’s use of Huawei mapping services, a concern outside the TFTC’s competition remit, but hardly a tailwind in Taipei.

What foodpanda Taiwan is worth, and to whom

The prize is substantial. Foodpanda has operated in Taiwan for about 12 years, with 3-4 million users and 100,000 merchants, and generated US$1.8 billion in gross merchandise value last year. The deal would have handed Grab just over 50 per cent of the market and a presence in 21 cities.

For Grab, the setback is strategic rather than financial. The company has been on a buying spree, from the US$425 million Stash acquisition to its move on Atome, and Taiwan was supposed to prove that the playbook honed across its eight Southeast Asian markets could travel. That proof must now wait. Investors who have questioned the quality of Grab’s profits may not mourn a US$600 million cheque that stays in the bank.

For Delivery Hero, the options are narrowing. An appeal is one route. A third buyer is another, but it would need to be a well-funded operator with no Uber ties. Meituan’s Keeta, which overtook foodpanda in Hong Kong within 29 months of launching, is the obvious candidate, although a Chinese-owned platform would face political headwinds of its own in Taiwan. Asked whether foodpanda might simply leave the island, a TFTC official called the question hypothetical.

The Southeast Asian mirror

Southeast Asian founders and investors should watch closely, because the Taiwan ruling previews questions closer to home. If Uber’s Delivery Hero deal closes, foodpanda’s operations in markets such as Singapore, Malaysia and the Philippines would fall under Uber’s roof. That would put Uber back in direct competition with the company it sold its regional business to in 2018, while remaining one of that company’s biggest shareholders. Regulators in the region will have to decide whether that counts as rivalry or a family dinner.

Also Read: GoTo’s first profit signals a fintech-driven future

And with Grab-GoTo merger talk resurfacing every few months, Taipei has shown that Asian competition authorities are now willing to look past market-share tables to the cap table.

Grab chose Taiwan to prove it could grow beyond Southeast Asia. Its biggest obstacle turned out to be the shareholder it inherited from its most famous deal.

The post Taiwan kills Grab’s US$600M foodpanda deal, and Uber’s shadow is the reason appeared first on e27.

I knew within the first two minutes of glancing through the deck.

A strategy workshop for our senior stakeholders had just wrapped. The session itself was good: the partner facilitating it followed our brief, the debate was sharp, and an AI tool captured the notes. When the deck arrived, I opened it and felt that familiar dip. Clean layouts. Confident headings. Icons beside every bullet. And almost nothing that sounded like the room we had just sat in.

I can’t prove how it was made, and I won’t pretend to. But the output looked like what happens when notes go in one end of a tool and a deck comes out the other, and nobody with judgement reads it as the audience would.

What the AI got right

Capture was mostly there based on what could be heard visibly. Almost every comment was transcribed, and the notes were more complete than anything a human note-taker would have produced. If you still think the risk with AI is that it can’t capture pronunciation properly, you are a year behind, especially if you are using a fit for purpose transcription tool.

The deck is where it fell apart, and the failure came in four forms.

• Contextual failure. Several inputs we had spent real time on never reached a slide though they were transcribed. The context was missing or misunderstood, which means the human-in-the-loop was clearing missing when reviewing the outputs.
• Misconstrued inputs. This one worries me most. One point had been softened into a generic statement, and another had its emphasis flipped. A missing idea is perhaps due to a missed captured but the classification of info under the wrong category is a human oversight failing to validate AI’s outputs.
• No narrative. The deck was a tidy inventory of topics. There was no argument, no sequence of “therefore,” and no answer to “so what?” A workshop deck exists to move a group from where they started to what they now believe. This one just reported that a conversation had occurred.
• Decorative design. The visuals were there to look polished, not to say anything. A chart with no point. A framework graphic that framed nothing. Design should carry positioning and tell the story, and here it sat on top of the content like wallpaper.

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

This is not a vendor story

It would be easy to write this as “my partner used AI badly.” But I use these tools constantly, and I have shipped drafts that weren’t ready, so the point is not that I’m exempt.

Research backs up what many of us have experienced. A Stanford and BetterUp Labs study, published in Harvard Business Review, gave this phenomenon a name: “workslop”, AI-generated output that looks like finished work but pushes the effort of fixing it onto the person who receives it.

That is exactly what happened here. The partner saved time, and my team inherited the cost. We spent hours checking the deck against our own notes, rewriting the narrative, and working out which statements were ours and which were the tool’s best guess of our intent.

What I do now

Two things changed in how I work, and in how I brief partners.

• First, I run a traceability check before anything AI-assisted leaves my hands. Every input from the session must map to a slide, or I must be able to say why it was dropped. It is tedious, and it catches what a skim never will.
• Second, I ask one question of every slide: what should the audience believe after seeing this? If I can’t answer in a sentence, the slide is decoration, and it goes or gets rebuilt.

Also Read: Employers are buying AI tools. Their managers are being left behind

With partners, the briefing conversation has shifted too. I now ask who the named reviewer is on any deliverable, and whether outputs will be checked against source inputs before they reach us. It is a slightly awkward question. It is far less awkward than the conversation after a bad deck.

The decision that stays human

Here is what I keep coming back to. AI can capture, summarise and format. It cannot decide what mattered most, or what the room really meant, or what story will land with this particular group of stakeholders on this particular day. That is judgement, and judgement needs a human owner.

The tool did not ship that deck. A person did. “AI slop” is rarely a tool failure. It is a sign-off failure: someone decided the output was good enough without reading it as the audience would.

So use the tools. Let them take the notes, and even draft the first version. But before you send anything, read it as the person who wasn’t in the room, and ask whether it would still make sense to them. If you wouldn’t put your name on it, don’t send it.

—

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.

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Bitcoin rose 1.51 per cent to US$84,055.22 in 24 hours, and Ethereum rose 0.97 per cent to US$2,471.00. The total crypto market cap rose 1.36 per cent. A rebound in technology and AI stock sentiment lifted risk appetite across both assets. Bitcoin showed a 79 per cent correlation with the S&P 500, and its 24-hour correlation with the S&P 500 ETF SPY was 0.79. That shared direction matters. My view is that this rally is real but narrow and derivative-heavy. It supports a cautiously bullish stance, not a conviction breakout call.

The initial driver came from technology and AI sentiment. SpaceX revenue soared 161 per cent, and Super Micro Computer offered positive guidance. Those earnings signals revived broader risk appetite, and that optimism flowed into crypto. Bitcoin’s tight link to the S&P 500 confirms that its short-term direction remains tied to traditional market sentiment, especially around growth and technology narratives.

The next test comes from upcoming earnings at major technology firms such as Meta and Tesla. Those reports could sustain the current mood or reverse it. If you have been reading what I wrote recently, I said to treat technology earnings as a near-term macro input, not background noise.

Bitcoin also received a mechanical push from a short squeeze. The asset saw US$84.26 million in total liquidations over 24 hours, and shorts made up 92 per cent of that amount. Rising prices forced leveraged short sellers to buy back positions, and that forced buying created a feedback loop that accelerated the upward climb.

This matters because derivative market mechanics amplified the rally, not just organic spot demand. That distinction shapes my view. A squeeze can drive a fast climb, but it can also increase volatility and leave the market vulnerable once forced buying runs out.

Watch for sustained high funding rates or a sharp contraction in open interest, either of which could signal the squeeze is running out of fuel. This is more tactical rather than structural. A squeeze can pull price toward resistance, and it can also fade once the forced buying stops. I would give more weight to spot demand than to liquidation data when judging whether the rally can last.

Also Read: Bitcoin at US$82,829: Why a 95% correlation with the S&P 500 should worry long term believers

On price structure, Bitcoin is testing the upper boundary of a well-defined US$80,000 to US$87,000 range. The seven-day simple moving average sits near US$83,100. If Bitcoin holds above that level and breaks US$87,000, it could target US$88,000. A rejection could bring a retest of the US$80,000 support. The near-term triggers are clear.

A daily close above US$84,200 would confirm near-term bullish momentum, while a close below US$82,000 would signal bearish momentum. The key macro trigger is the Federal Reserve’s rate decision on October 28. Bitcoin is consolidating and awaiting a macro catalyst for its next decisive move. I would want a decisive close above US$87,000 after the Fed announcement before treating this as a breakout rather than a range trade.

That plan keeps risk defined because the range gives both sides a clear line in the sand. My humble advice is not to chase the climb into US$87,000 before the Fed meeting, and I would not treat a single close above US$84,200 as proof of a new trend. The central bank decision deserves more weight than one session of price action.

Ethereum has its own supply-side catalyst. On October 20 to 21, Tom Lee’s Bitmine staked an additional 159,728 ETH worth approximately US$390 million. That brought its total staked holdings to about 5.23 million ETH. This action locks a significant portion of supply, reduces potential sell pressure, and signals strong institutional belief in Ethereum as a yield-generating asset. Large long-term holders are actively choosing to earn yield rather than sell.

That provides underlying demand support. The watch point is continued growth in the staking queue, which would indicate sustained demand to lock supply. It gives Ethereum a firmer floor than pure sentiment alone would provide, though a supply lock does not guarantee an immediate breakout.

Ethereum also benefited from the broader market lift. The total crypto market cap rose 1.36 per cent, led by Bitcoin’s 1.51 per cent gain. A rebound in global technology and AI sentiment supported the move, with strong earnings forecasts from companies such as SpaceX helping the mood. Ethereum saw US$47.99 million in liquidations over 24 hours, with shorts comprising the majority.

That suggests a squeeze added momentum. Ethereum did not rise alone. It benefited from a risk-on shift in correlated assets. The key watch point is how US equity markets react to upcoming technology earnings from Meta and Tesla, as those reactions can provide continued correlation cues.

Also Read: Institutional buyers face a massive test at the US$81,000 Bitcoin support level

Ethereum now faces immediate resistance at the psychological US$2,500 level. A sustained break above that level, especially on a daily close, could open the path to the 30-day simple moving average at US$2,579. The scheduled activation of Ethereum’s Hoodi testnet upgrade on October 26 could renew developer and investor focus.

Failure to hold US$2,470 risks a pullback toward the recent low near US$2,380. A daily close above US$2,500 would confirm bullish momentum, while a drop below US$2,380 would signal renewed weakness. My view is that Ethereum needs spot buying volume to accompany a break above US$2,500. Without that volume, a rejection would suggest the rally came primarily from derivatives positioning and short covering.

My overall point of view is cautiously bullish on both assets, with a preference for patience over chasing. Bitcoin’s improved risk sentiment and a derivatives squeeze provided a clear catalyst, but the price stayed within a multi-week range and showed little breakout conviction. Ethereum’s substantial supply lock-up and favourable macro backdrop provide a solid floor, but it must break above US$2,500 to sustain momentum.

The key watch is whether Bitcoin can decisively close above US$87,000 after the Fed’s policy announcement and whether Ethereum can break US$2,500 with rising spot volume. If those conditions fail, both markets risk reverting to range midpoints or retesting support. The data supports a constructive stance, but not complacency. Macro sentiment and spot demand matter more than a short squeeze, and I’d track those most closely.

My stance leans toward selective exposure rather than aggressive accumulation. The data point to a constructive backdrop, but also to fragile momentum. A trader can respect the range, wait for confirmation, and let the Fed decision and spot volume decide the next leg.

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 92% of Bitcoin liquidations were shorts: What happens when the squeeze ends? appeared first on e27.

Until this summer, the morning numbers at LDC Import-Export Co., Ltd. were built by hand.

LDC is a Lao distribution and retail group. It started in Vientiane in 2011, distributing local and imported goods, and its retail arm now runs 39 convenience stores across seven provinces, including Jiffy shops on PTT fuel forecourts and its own Sky Mart chain. The ERP behind those stores keeps the business in 52 databases that the company could only read through manual spreadsheet exports. Every morning the business-analysis team spent hours assembling the Excel report packs from those exports. The cost of empty shelves across the chain had never been put in money, and a question like “which items sold at zero cost last week” had no one to ask.

I’m the CEO and co-founder of QUEST EDTECH, and LDC is one of its clients. I took the work on because LDC was losing money it couldn’t see, and its analysts spent the first hours of every morning assembling numbers instead of using them.

Since May, my company has rebuilt that layer with AI coding agents working under my direction. A chain now restores the ERP at 4:45am each morning, rebuilds a warehouse and emails the packs by 7:45am with nobody awake, and a check at one in the afternoon ties 160,000 cells back to the source system. The analysts got their one to two hours a morning back, several hundred hours a year.

The first replay of the year found about US$570,000 of sales lost to empty shelves, more than half of it while the item sat in LDC’s own distribution centre, plus a duplicate-sales event the ERP had hidden. The AI running cost LDC carries for all of it is about US$100 a month. Five department files that staff used to compile by hand now rebuild themselves every night.

Before this year, the business-analysis team assembled the morning packs by hand, with Power Query pulls, SUMIFS across every branch and copy-paste from the ERP. When I proposed live dashboards instead, the answer was that the analysts need yesterday’s number in the format they know, and that stays.

So the format was fixed before a line of code existed and the daily reports are the original manuscript. Agents rebuilt the packs as codes, by mid-July they had loaded the ERP estate into a warehouse of about 60 million rows, the nightly chain has run unattended since 24 August via automation.

QUEST EDTECH realised that three things had to be fixed before the output was usable, and none of them were codes. The first fix was definitions, because the “online sales” column in one pack had been carrying wholesale accounts and ran several times too high. The definition that fixed it came from the analysts’ own requirements document, because the ERP codes credit returns differently from cash ones and the second code had never been filtered. Both fixes came from asking the people who own the numbers, and that’s where I’d start next time.

The second fix was time, after a date computed on a server that runs on UTC produced a pack with the wrong day’s data. The rule now is to compute a Bangkok day on a box set to Bangkok time. The third fix was a rule for the agents themselves, using the semantic layer derived from the database as the fact, then write the code. Agents read the database’s own schema before they name a column.

Also Read: AI agents broke into 100 firms for pocket change. SEA’s online shops fit the target profile

When the AI was confidently wrong about LDC’s numbers

Once nobody was assembling the packs by hand, nobody was looking at each number on its way to LDC either. In August every target-achievement figure in one pack read exactly 100 percent, which should’ve been the first clue. LDC’s target workbook is a rolling file, and when a month closes, its target is overwritten with the actual result.

The agents couldn’t know that, so they loaded actuals as targets and the pack was checking itself. What caught it was a test that rebuilt the targets from the sales data and found the two were identical. That same month I gave the agents the rule I’d give any team verify the fact, then write the code.

What AI informs but can’t decide

Before I could put the cost of LDC’s empty shelves in money, someone had to decide what an empty shelf is. Four defensible definitions of out-of-stock on the same data gave 0.6, 10.1, 67.2 or 77.3 percent. The agent could compute all four in minutes, but it couldn’t tell me which one the business should manage. I settled it with the rule the buyers already work to, from the supply-chain team’s own workbook. The US$570,000 of lost sales is counted on that rule’s zero-stock test.

What reaches whom is a human call too, and the AI “chief strategy officer” I set up to write weekly briefs for LDC’s CEO showed why. Its first version flagged four branches as the week’s news, and all four were already red in the pack the analysts read every morning. I rebuilt it as a decision sheet with an owner, a date and the amount at stake on each line, and who gets it is my call.

Also Read: Tokens are the wrong yardstick for AI agents, says JFrog’s Sunny Rao

What stopped, and what it has been worth

The analysts stopped building the packs, and five department spreadsheets that staff uploaded by hand were retired once a system source replaced them. I stopped waking up for failures too. In September I told the agents that when something breaks between 4 am and 9 am I need them to fix it, not notify me, since silicon doesn’t need sleep, and a DevOps agent now runs four checks a day and re-runs known-safe steps itself.

The scale of what agents build is the part I’d have doubted a year ago: about 240 dashboard cards in one three-day stretch in September, at roughly 4.1 minutes of my time per card, and 766 saved questions and 198 dashboards on the BI site against the client’s requirement of 76 reports.

The honest way to count the money is in what the system has found, because realised savings are LDC’s to report. With AI, LDC has identified more than half was lost while the distribution centre had the item in stock. Annualised, that’s roughly US$770,000, which is why the next phase is a replenishment fix rather than another report.

Who does what and the outcome

On QUEST EDTECH’s side the agents work in lanes, with Claude Code building and operating. The bigger change is on LDC’s side, where the analysts got Claude Desktop skills that turn a question in Thai into SQL. As of October, the BA lead added her own card to the finance dashboard, written through that skill with AI in Thais and Laos, and the build was changed so her cards survive every rebuild. The people who used to assemble the numbers manually now ask questions to AI and it just builds reports, dashboard and identifies with simple languages instead of power query or even a line of SQL.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post How a 39-store retail chain in Laos moved from Excel to AI agents appeared first on e27.

Southeast Asia has a talent for making climate pledges sound like wedding vows: grand, emotional and frequently broken. Net-zero deadlines stretch to 2050 and beyond, yet coal still dominates several national grids, petrol motorbikes still choke the cities and mangroves still vanish faster than ministers can name them. Into that gap has stepped a scrappy generation of founders who would rather build than promise.

We combed e27’s startup database for climate tech companies founded between 2015 and 2026. The result is 30 startups worth watching. Some are flush: VFlowTech raised US$20.5 million for its vanadium flow batteries, and MediSun Energy closed an US$8.75 million seed round at a US$44 million valuation. Others survive on grants, angels and sheer stubbornness. Between them, they are turning chicken manure into coal substitutes, used cooking oil into biodiesel and idle backup batteries into revenue.

Also Read: Passing 1.5°C global warming: Performative ESG is no longer acceptable

None of them will save the planet alone. Collectively, though, they are a better barometer of the region’s climate ambition than any COP communiqué.

Energy, solar and storage

The region’s biggest emitter is its power sector, so the most capital has flowed here, led by VFlowTech’s US$20.5 million round.

1. VFlowTech

Builds vanadium redox flow batteries for long-duration energy storage.

• Founded: 2018
• Founders: Avishek Kumar, Arjun Bhattarai
• Headquarters: Singapore
• Funding: US$3M pre-Series A (2021, led by Wavemaker); US$10M Series A (2023, led by Real Tech Holdings); US$20.5M (2025, led by Granite Asia)

2. GetSolar (formerly Solar AI Technologies)

Runs a rent-to-own rooftop solar programme for homes and SMEs.

• Founded: 2020
• Founders: Bolong Chew, Gérald Chablowski, Luke Ong
• Headquarters: Singapore
• Funding: S$450K pre-seed (2020, ENGIE); US$1.5M seed (2023, led by Earth Venture Capital)

3. Verta Bioenergy

Turns farm and livestock waste into biomass pellets that replace coal in industrial boilers.

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

• Founded: Not disclosed (incubated under EDB’s Corporate Venture Launchpad)
• Founders: Jeremy Tan (co-founder and CEO)
• Headquarters: Singapore; plant in the Philippines
• Funding: S$1.1M (US$845K) seed (2025; ENGIE Factory, Wright Partners, AlphaGen VC)

4. Amplicity Energy

Software that turns idle backup batteries at industrial sites into grid revenue.

• Founded: Not disclosed
• Founders: Not disclosed
• Headquarters: Singapore
• Funding: US$1M seed (2026, ENGIE-backed)

5. SolX Technologies

Provides digital energy services that promise electricity savings.

• Founded: 2022
• Founders: Sergius Santos, Matt Levin Tan, Bradley Cua
• Headquarters: Makati, Philippines
• Funding: US$780K seed (2024; UntroD Capital Asia, Leave A Nest, Real Tech Holdings, Foxmont)

6. Arysun Energy

Sells affordable solar starter kits to middle-class households.

Also Read: EVs to make up over half of Singapore vehicle sales in 2026, BMI says

• Founded: Not disclosed
• Founders: Ng Aik Phong (listed on e27 profile)
• Headquarters: Singapore; selling in Indonesia
• Funding: US$575K pre-seed (2025; First Move, Wavemaker Partners)

7. TailorTech

Power management system that cuts fuel use at off-grid generators.

• Founded: Not disclosed
• Founders: Brothers Ts. Dr Indera Shaiful Baharudin and Ts. Indera Shafiq
• Headquarters: Malaysia
• Funding: US$110K pre-seed (2024, Antler)

8. Altent Renewables

Converts wet waste into clean fuels.

• Founded: 2022
• Founders: Tan Wee Han (founder and CEO)
• Headquarters: Singapore
• Funding: Not disclosed

E-mobility and clean logistics

Two-wheelers and delivery fleets are where the region’s transport transition will be won or lost, and charging infrastructure remains the bottleneck.

9. Blitz Electric Mobility

Operates an EV last-mile delivery fleet with AI routing and driver financing.

Also Read: VinFast’s ride-hailing arm Green SM takes on Jakarta’s ojek economy with e-scooters

• Founded: 2019 (operating since 2022)
• Founders: Saivya Chauhan
• Headquarters: Jakarta, Indonesia
• Funding: US$2.6M seed, including extension (2024; Third Derivative, Iterative, ADB among backers); undisclosed pre-Series A led by Vynn Capital (2025)

10. ChargeSini

Builds and operates EV charging infrastructure.

• Founded: 2022
• Founders: James Goh and partners from the automotive and tech sectors
• Headquarters: Malaysia
• Funding: US$1.25M seed (2023, via pitchIN)

11. Aerion Motors

Building a premium e-motorcycle brand for Indonesia, with production planned for Q4 2026.

• Founded: Not disclosed
• Founders: Adrian Cheng (listed on e27 profile)
• Headquarters: Singapore
• Funding: US$150K angel (2025)

Carbon accounting, carbon markets and climate finance

Disclosure rules are tightening across ASEAN exchanges, which has turned emissions data from a CSR nicety into a compliance product.

12. Unravel Carbon

AI carbon accounting software for enterprise supply chains.

• Founded: 2021
• Founders: Grace Sai, Marc Allen
• Headquarters: Singapore
• Funding: US$7.38M seed (2022, led by Surge), taking total to US$8.75M; undisclosed round from Japan Energy Capital and TIS (2025)

13. CarbonEthics

Develops blue carbon, peatland and forest restoration projects.

• Founded: 2019
• Founders: Bimo Soewadji (Agung Bimo Listyanu), Jessica Novia
• Headquarters: Jakarta, Indonesia
• Funding: US$220K pre-seed (2023; Spiral Ventures, Ecoxyztem); US$2.1M seed (2024, led by Intudo Ventures)

Also Read: EVs gain ground in the Philippines as fuel costs squeeze drivers

14. Refy

Asset-based financing for small and mid-scale green projects.

• Founded: 2023
• Founders: Ed Chin
• Headquarters: Singapore
• Funding: US$525K pre-seed (2024, Wavemaker Impact)

15. CREX

Turns hotels’ sustainability data into AI-ready signals that drive bookings.

• Founded: Not disclosed (first round in 2022)
• Founders: Sam Hon (listed on e27 profile)
• Headquarters: Singapore
• Funding: US$750K angel (2022); US$300K seed (2024)

16. Carbonwize

Audit-grade emissions reporting linked to green financing.

• Founded: 2022
• Founders: Natalie Lerthatasilp (listed on e27 profile)
• Headquarters: Bangkok, Thailand
• Funding: US$265K pre-seed (2023); about US$1.73M raised in total, per PitchBook

17. Carbon GPT

AI-driven greenhouse gas accounting across all scopes.

• Founded: Not disclosed (on e27 since 2024)
• Founders: Matthew Wong (listed on e27 profile)
• Headquarters: Malaysia
• Funding: US$20K Cradle Fund grant (2023); US$120K angel (2024)

Circular economy and waste

Waste is the least glamorous corner of climate tech and, arguably, the one with the fastest payback: methane from landfills and open burning is an immediate problem.

18. Rekosistem

End-to-end waste collection, sorting and material recovery.

• Founded: 2018 (formally launched 2021)
• Founders: Ernest Layman, Joshua Valentino
• Headquarters: Jakarta, Indonesia
• Funding: US$5M (2023, led by Skystar Capital); US$7M Series A (2025; led by Saratoga Investama Sedaya and K3 Ventures)

19. Remind

Collects and processes e-waste and recovers metals.

• Founded: 2020
• Founders: Muhammad Dzikri Ahira Soefihara
• Headquarters: Tangerang, Indonesia
• Funding: US$1.3M (2025; co-led by Bali Investment Club and BEENEXT, with Spiral Ventures)

20. GEPP Sa-Ard

Digital waste-tracking and trading platform for businesses.

• Founded: 2017
• Founders: Dome Boonyanurak, Mayuree Aroonwaranon
• Headquarters: Bangkok, Thailand
• Funding: US$300K pre-seed (2020); backers include Delta Electronics Thailand, depa and AIS The StartUp

21. Arus Oil

Collects used cooking oil and supplies it to biodiesel refineries.

• Founded: 2017
• Founders: Syazwan Majid
• Headquarters: Subang Jaya, Malaysia
• Funding: Pre-seed, angel, seed and debt rounds totalling about US$192K (2021–2023); 1337 Ventures Accelerator Fund (2025)

Food, agriculture and biomaterials

Agriculture and land use drive a large share of the region’s emissions, and these startups attack it from the farm, the feed trough and the lab.

Also Read: Sustainability reporting is not the goal. Better business decisions are

22. Agros

Solar-powered irrigation and soil products for horticulture farmers.

• Founded: 2019
• Founders: Max Nelen
• Headquarters: Singapore; operations in Indonesia
• Funding: US$2.7M pre-Series A (2023); US$4.25M Series A (2025; co-led by Gaia Impact, Schneider Electric Energy Access Asia, Wavemaker Impact)

23. Terra Oleo

Uses microbes to make palm oil and cocoa butter alternatives from agro-industrial waste.

• Founded: 2023
• Founders: Shen Ming Lee, Boon Uranukul, Min Hao Wong
• Headquarters: Singapore
• Funding: US$3.1M (2025; ADB Ventures, Better Bite Ventures, Elev8.vc, The Radical Fund)

24. AgriG8

Credit platform that links rice-farmer loans to sustainable practices.

• Founded: 2021
• Founders: Aries Dwiputera, David Chen
• Headquarters: Singapore
• Funding: US$900K seed (2024)

25. Virdalis

Duckweed-based protein for animal feed, as an alternative to soy.

• Founded: Not disclosed
• Founders: JM Aujero (listed on e27 profile)
• Headquarters: Singapore
• Funding: US$700K pre-seed (2026)

26. Cleantech and Beyond

Advanced functional materials for sustainability uses.

Also Read: N&E Innovations nets US$1.7M to turn cashew husk waste into fresh produce protection

• Founded: Not disclosed
• Founders: Not disclosed
• Headquarters: Thailand
• Funding: US$100K pre-seed (2024); US$50K seed (2025)

Water, buildings and efficiency

Cooling, desalination and industrial water treatment are energy hogs in the tropics, which makes efficiency a climate play in its own right.

27. MediSun Energy

Brine management and osmotic energy technology for desalination and heavy industry.

• Founded: Not disclosed
• Founders: Joseph Chua, Dusun Kim
• Headquarters: Singapore
• Funding: US$8.75M seed (2024, led by Vynn Capital, at a US$44M valuation); further seed funding of about US$3M reported (2026)

28. Aprisium

Real-time contaminant detection, including PFAS, for water and industrial fluids.

• Founded: 2021
• Founders: Raghav Narayan, Dr Lars-Henrik Skjolding
• Headquarters: Singapore
• Funding: US$800K seed (2023, Cocoon Capital); US$7M Series A (2024; led by Deep-Tech Venture Partners and Cocoon Capital)

29. BIZSU

Passive add-on that cuts air-conditioning energy use.

• Founded: 2019
• Founders: Brice Degeyter
• Headquarters: Singapore
• Funding: About US$250K angel (2021); US$100K angel (2025)

30. intlife

IoT and AI energy efficiency for homes and offices.

Also Read: Life Lab Resources grabs US$1M to turn food waste into aquaculture feed

• Founded: Not disclosed
• Founders: Kok Huang Er
• Headquarters: Malaysia
• Funding: US$25K Cradle Fund grant (2023)

What the list tells us

Read the headquarters lines and a lopsided map emerges. Seventeen of the 30 are based in Singapore, against five in Malaysia, four in Indonesia, three in Thailand and one in the Philippines. Vietnam, the region’s fastest-rising factory floor, does not appear at all. Singapore’s grants, incubators and corporate venture arms clearly work, but the city-state is not where most of the region’s emissions come from.

The cheques are also small. About half of these companies have yet to disclose a single round larger than US$1 million. A battery-swapping network or a desalination plant cannot be built on angel money. Until growth-stage investors back climate hardware with the conviction they once reserved for super apps, many of these founders will stay stuck proving the same pilot over and over.

The post Thirty climate tech startups in Southeast Asia that would rather build than pledge appeared first on e27.