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.

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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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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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