For many companies expanding into Southeast Asia, hiring is no longer the hardest part. The bigger challenge is often what comes after: setting up a local entity, complying with employment regulations, running payroll and moving money across borders without unnecessary delays or excessive costs.

Singapore-headquartered TalentHero, co-founded by Oliver Lewis (CEO) and James Miles (COO), is looking to address these challenges through a new strategic joint venture in Thailand, alongside a proposed business partnership with Thai cross-border payments provider DeeMoney.

Also Read: 1982 Ventures joins Limited’s US$18.5M seed round to simplify cross-border business banking

The joint venture, established between TalentHero and Aswin Phlaphongphanich in his capacity as a private strategic investor, will focus on developing Employer of Record (EOR), recruitment, payroll administration and related workforce solutions for businesses operating in Thailand.

Separately, the new venture intends to explore payment partnerships, such as with DeeMoney, to support cross-border payments, international remittances and corporate payouts for its recruitment and EOR clients.

The agreement was signed in Bangkok on 2 October 2026, with Lewis and Miles representing TalentHero. Phlaphongphanich, who is also Co-Founder and CEO of DeeMoney, participated in his private investment capacity.

DeeMoney CFO Dylan Keota also attended, reflecting the parties’ intention to explore a commercial payments collaboration. The transaction was facilitated by Michael Gui, Partner at Copper Advisory.

At its core, the new venture targets a practical pain point. Companies want to hire in Thailand without necessarily establishing a Thai subsidiary from day one. At the same time, employers operating across multiple jurisdictions need reliable employment administration, payroll support and efficient payment processes.

“Building a team in a new market involves more than finding the right people. Employers also need reliable employment support and a practical way to manage payroll and payments,” said Lewis. “Through our new venture in Thailand, and by exploring commercial payment solutions with DeeMoney, we aim to make that journey simpler for businesses entering and growing in the market.”

Why EOR is becoming part of the expansion toolkit

Employer of Record, or EOR, has become a familiar model for startups and growth companies entering new markets. Under this arrangement, a business hires local employees through a third-party local employer, which handles employment administration, payroll and HR compliance. The client manages the employees’ day-to-day work, while the EOR handles the legal and administrative employment layer.

Also Read: TrueMoney and Fireblocks want Thai users on blockchain rails without them noticing

The model gained momentum during the remote-work boom, but its use in Southeast Asia is also tied to a more durable trend. Companies want to build distributed teams across multiple markets without carrying the cost and complexity of setting up local entities everywhere.

For Thailand, the proposition is timely. The country has long been a regional base for tourism, manufacturing, trade and services, but it is also positioning itself more strongly in digital industries. International companies entering the market need local hiring support, while Thai talent is increasingly part of regional and global teams.

TalentHero’s role in the joint venture will be to provide employment infrastructure and operational expertise. The company offers EOR, recruitment, global payroll and outsourcing services, combining talent acquisition with employment administration and operational support.

That matters because hiring is rarely a single transaction. A foreign company may first need to recruit a local country manager, then build a sales, operations or technical team, and later decide whether to incorporate locally. EOR can act as a bridge during that process, particularly for startups testing product-market fit or enterprises launching a regional function.

Where cross-border payments enter the picture

An additional opportunity being explored is the connection between employment services and international payment infrastructure.

DeeMoney, founded in 2017, specialises in international money transfers and cross-border payment services for individuals and businesses in Thailand.

Rather than participating as an equity shareholder in the new joint venture, DeeMoney is being considered as a separate commercial payments partner, with discussions focused on potential applications of its existing payment capabilities to the recruitment and EOR sectors.

These could include international remittances, cross-border business payments and payroll-related payout infrastructure, subject to applicable regulations, commercial arrangements and operational feasibility.

Thailand is both a destination and a source market for migrant workers, expatriates and regional business activity. For employees supporting families overseas, or companies managing payments to international contractors, suppliers or distributed teams, reliable payment infrastructure is an important operational consideration.

“Employment services and cross-border payments are closely connected, particularly for businesses operating internationally,” said Phlaphongphanich. “We see an opportunity to explore how DeeMoney’s established payment capabilities could complement TalentHero’s workforce solutions, helping businesses manage international payments more efficiently.”

The opportunity is not simply to offer more services under one roof. If commercially viable, closer coordination between employment administration, payroll processes and established international payment providers could reduce operational friction for companies building teams across Southeast Asia.

Any payment services arising from the proposed collaboration would remain subject to the relevant regulatory requirements and be delivered through appropriately licensed providers.

A crowded but growing market

The new venture will not be operating in an empty field. Globally, EOR and payroll platforms such as Deel, Remote and Oyster have built large businesses helping companies employ people across borders. In Asia, players including Multiplier and other HR-tech providers are competing to serve companies hiring across the region.

Also Read: Circle to acquire Tazapay for US$400M as USDC push moves into cross-border payments

On the payments side, DeeMoney operates in a similarly competitive landscape. Wise, Airwallex, Nium, Tranglo and traditional banks all serve parts of the cross-border payments market, while local fintechs and remittance providers compete on price, speed and coverage.

What could differentiate TalentHero’s new Thailand venture is the combination of international employment expertise, local market knowledge and potential access to established commercial payment infrastructure.

Global platforms often sell broad geographic coverage, but companies entering Thailand may still need local nuance. That includes employment administration, payroll timing, worker expectations, compliance processes and payment corridors that match actual business requirements.

Integration will nevertheless be important. EOR and recruitment are operationally demanding businesses, while international payments require appropriate licensing, risk controls and reliability. Any partnership must therefore balance customer convenience with regulatory compliance and operational effectiveness.

Thailand as a strategic growth market

For Southeast Asia’s startup ecosystem, TalentHero’s expansion reflects a broader shift in business infrastructure.

The first wave of digital tools helped companies sell online, communicate remotely and accept payments. The next wave is increasingly focused on essential operational functions: hiring, employment compliance, payroll administration and cross-border business payments.

Thailand offers an attractive environment for this approach. Its position within regional trade and investment flows, established industrial base and growing digital economy make it a relevant market for companies building international teams.

The joint venture also represents another step in TalentHero’s broader Southeast Asian expansion strategy, building on its existing regional operations and ambition to simplify international hiring and workforce management.

Also Read: Why investors often back Vietnamese startups more aggressively than Thai peers

For founders and operators, the appeal is straightforward. If they can enter a new market, hire efficiently, remain compliant and manage employment-related payments reliably, they can focus more resources on customers and growth.

The new Thailand venture is currently progressing through its establishment and operational preparations. The parties have not publicly disclosed its ownership structure or financial terms, and any commercial payment collaboration with DeeMoney remains subject to further discussions and applicable regulatory requirements.

As Southeast Asian businesses become increasingly regional, and international companies look deeper into the region for talent, employment infrastructure and cross-border payment services are becoming more interconnected.

Through its new Thailand venture, TalentHero aims to play a growing role in supporting that transition.

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Every few weeks, another report tells the same story about Southeast Asian tech: capital has fled, deals have dried up, and the region’s startup engine has stalled. The most recent numbers make the case forcefully. Zero dedicated regional private equity funds reached final close in the first half of 2026.

Three pan-Asian mega-funds — EQT, Blackstone, and Bain Capital — absorbed US$39.2 billion, or roughly 85 per cent of all Asia Pacific PE capital raised in that period. Regional tech funding had already fallen 79 per cent between 2022 and 2024, from US$10.1 billion to US$2.2 billion, and exit values dropped a further 32 per cent in 2025.

Read those figures and the conclusion writes itself: the building has stopped. But that conclusion only holds if institutional capital is still the right instrument for measuring who is building.

The concentration problem sits inside the AI number too

Even the one bright spot in the data is thinner than it looks. Southeast Asia’s AI startups raised US$4.1 billion in 2026 through July, more than double the US$2 billion raised in all of 2025. That sounds like recovery, until you notice that Kling AI’s single US$2.8 billion Series D accounts for about 68 per cent of it. Strip that one round out, and native AI companies across the entire region raised roughly US$1.3 billion this year — a real number, but not the turnaround the headline implies.

So the capital that does exist is concentrating twice over: regionally, into a handful of pan-Asian vehicles, and within Southeast Asia’s own AI sector, into a handful of outsized rounds. Both trends are true. Neither tells you much about whether people are actually building.

Also Read: Light liquidations and flat funding: Is Bitcoin about to explode?

A second economy isn’t showing up on any cap table

While institutional funding was contracting, a different kind of activity was compounding. The market for AI-assisted, natural-language software building — often called vibe coding — is now estimated at US$4.7 billion and growing at roughly 38 per cent a year, with 63 per cent of active users being non-developers rather than trained engineers. The same reporting puts the typical span from a first idea to a paying customer at about four weeks — a cycle that requires no seed round, no pitch deck, and no line item in any PE report.

This is not a story about tools making bad builders look competent. It is a story about who gets to start. A product manager, a designer, or, increasingly, someone with no formal technical training at all can now ship and monetise a working product inside a month, entirely outside the instruments used to track “the startup ecosystem.” None of that activity registers as a data point in the reports currently used to declare the region’s tech sector shrinking.

I can speak to this from experience. I came up through the Normal Technical stream at school rather than a computer science degree, and spent years working as a paramedic before I started building software by prompting. What began as one product, a consumer signal-intelligence platform under construction at OnTheRice, eventually led to a board and product-architecture role at SCAVerse, a Singapore culture-tech venture.It does not counter the funding data. It is one example of a pattern those figures can’t capture: builders who exist before, and sometimes without, a term sheet.

The asymmetry now being closed isn’t only between founders

The same shift is playing out one level down, between institutions and the consumers those institutions serve. The AI consumer insights market is projected to grow from US$5.3 billion in 2026 to US$12.15 billion by 2034, with retail and e-commerce applications already accounting for about 45 per cent of that spend. What that money is buying, in practice, is the ability to point institutional-grade pattern recognition — the kind previously reserved for analyst desks and trading terminals — at ordinary consumer decisions: what a price move actually means, which claim in a press release holds up, which trend is a signal and which is noise.

Also Read: A funding round can improve your metrics without improving your company

Historically, that gap belonged to whoever could afford a terminal, an analyst, or a research subscription. It is the same gap that separated a funded founder from an unfunded one. Both are narrowing for the same reason: the cost of building serious analytical tooling has fallen faster than the institutions measuring “innovation” have updated their instruments.

Why this matters more than the funding headlines

If the only lens applied to Southeast Asia’s tech health is PE fund closes and disclosed equity rounds, the region will keep reading itself as being in retreat, even as real building activity — by non-traditional founders, using AI-native tools, serving consumers directly — may be at a multi-year high. That mismatch has consequences. Policymakers calibrate support programmes off these numbers. Investors calibrate conviction off them. A region that under-counts its own builders risks under-investing in exactly the layer where its next founders are already working.

The instruments didn’t just miss the recovery. They may be measuring an economy that is quietly becoming smaller than the one actually running underneath it — and the builders who never needed a term sheet to start are not going to wait for the definition to catch up.

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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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Here is an uncomfortable finding for anyone building a 2027 marketing plan this month: audiences are warming to advertising, and marketers are growing less sure that theirs works.

That tension sits at the centre of Kantar’s Media Reactions study, published on 2 October and based on interviews with more than 800 senior marketers and 23,000 consumers. Only half of advertisers now feel they have the right balance between brand building and performance, down from 60 per cent a year ago. Confidence in the media mix slipped from 64 per cent to 57 per cent. And just 51 per cent say they are pulling actionable intelligence out of their data. Kantar’s own conclusion is blunt: the fix is sharper intelligence, not a bigger pile of data.

The hidden cost: The confidence gap

Call it the confidence gap: the distance between how much a team can measure and how sure it is about what to do next. It is easy to miss because every individual number looks healthy. Each platform reports its own engagement, its own attributed return and its own reach, and each one, read alone, says the money is working.

The trouble is that these numbers grade their own homework, and they often point the wrong way. Kantar’s creator research earlier this year found that platform engagement and real brand-building potential line up only about a third of the time. The number most teams use to pick creators misleads them two times out of three. Repeat that across search, social, retail media and video, and you get a dashboard that is busy, green and quietly uninformative.

Part of the gap is perception rather than performance. Kantar’s effectiveness data shows that channels working together drove 43 per cent of campaign performance between 2020 and 2025, up from 18 per cent before 2014. Marketers are getting better at making a fragmented mix pay. They just can’t see it clearly enough to defend it.

The moment it bites

Picture a regional head of marketing in Singapore in mid-October, three weeks from locking next year’s budget. Her quarterly review looks excellent. Paid social return on ad spend is up. Engagement on the creator programme has doubled. Cost per click fell in every market. Then the CFO asks the only question that matters: which of these moved revenue, and which can we cut?

Also Read: Why SEA’s retailers need to protect AI-powered customer experience with observability 

Nobody in the room can answer with confidence. Every channel can show a number that went up; none can show that the sales would not have happened anyway. So the budget rolls forward with a few percent shaved off everywhere, the same plan as last year, defended with the same slides.

That is the confidence gap turning into money. It is not one bad decision. It is a year of decisions nobody can explain.

The shift: A new channel nobody can measure yet

The gap is about to widen. Kantar finds that a net 75 per cent of marketers plan to increase investment in AI assistants in 2027, and 62 per cent expect generative AI to play a pivotal role in brand recommendations. Yet only 32 per cent of consumers use these assistants to research brands, and just 23 per cent trust what they recommend. Money is moving ahead of evidence.

The buy side already feels it. In the IAB’s outlook last month, 86 per cent of US buyers said they are changing how they measure media because of conversational AI and agents, and 45 per cent named comparing AI-driven and traditional customer journeys as their hardest measurement problem. Being found, cited and recommended in AI answers is turning into a goal in its own right, with no agreed way yet to count it.

What wins in 2027 is less measurement theatre and more signal discipline:

• Fewer, causal metrics. Pick the handful of numbers that track revenue or margin, and test them with holdouts instead of trusting each platform’s attribution.
• Prediction before spend. Score creative and audiences before the budget goes out, so the post-campaign report confirms a bet rather than explains it away.
• Category context. Read what competitors and audiences are doing across the market, not just your own funnel. A number only means something next to the market it sits in.

Why Southeast Asia feels it first

The confidence gap is wider here, for structural reasons. A regional team plans across half a dozen markets, several languages and a platform mix that changes at every border, from marketplaces to super-apps to messaging-led commerce. Global brands often hand local teams a direction from headquarters and a dashboard built for a single market, then ask them to defend spend line by line.

Also Read: AI chats are becoming the new starting point for online shopping

Agencies carry the same load with fewer people. An independent shop pitching against a network rarely loses on ideas; it loses on the depth of the read it brings into the room. That is why the insight stacks agencies build now matter as much as their creative. Whoever can explain the category, the audience and the competitive moves fastest earns the right to say what the numbers mean.

How the gap gets closed

The teams closing the gap are not adding dashboards. They are changing what they look at before they spend. As the founder of SOMIN, I see the same pattern across brands and agencies: read the category first, find the few signals that predict outcomes, and let those signals set the plan.

Two cases from our own work show how this plays out, and both are shared here as examples of the approach, not as proof the product works.

At Mothercare Singapore, the local team was tracking more than 4,000 category ads a year by hand and still guessing which content would land with Singaporean parents. Benchmarking the category by engagement per post rather than by volume showed that content about the real challenges of parenthood earned roughly 39 engagements per post, against about seven for festive gifting. With that read in hand, the team reported that time spent on research and ideation fell by 80 per cent, and that audience segmentation became three times more refined.

SAMY, an independent creative agency, used a similar approach to pitch a UK hospitality group against larger networks. Instead of leading with concepts, it walked in with a read of the client’s competitive set and an audience the category had left unserved, and won a multi-million-pound retainer. The agency now gets a click-through prediction on its creative before media money moves, which it reports at 78 per cent accuracy.

The three-number test

None of this requires a bigger martech stack. It requires the discipline to retire the metrics that flatter and keep the few that predict, and the confidence to explain that choice out loud: to the CFO, to clients and to the market. The leaders who do that in public, with a steady voice and the data behind it, will set the terms everyone else gets measured by.

So before the 2027 plan is locked: if you had to cut your dashboard to three numbers tomorrow, which three would you keep, and would your CFO pick the same ones?

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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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The cryptocurrency sector experienced a severe and sudden decline over the last 24 hours. Bitcoin fell 2.28 per cent to US$81,370.58. The broader digital asset ecosystem mirrored this downward trajectory, dropping 2.22 per cent to a total valuation of US$2.74 trillion.

An unexpectedly hot United States inflation report drove this widespread selloff across all major digital exchanges. The Bureau of Labor Statistics released the September Consumer Price Index data on October 14. This official government report showed the headline index rose 3.7 per cent year-over-year, beating the consensus forecast of 3.6 per cent.

Core inflation also surprised veteran investors by rising 0.3 per cent month-over-month, above the strict expectation of 0.2 per cent. These stubborn inflation numbers immediately altered investor sentiment and sparked a broad retreat from speculative risk assets. Retail traders and large institutional funds reduced their exposure to protect their capital.

Investors now anticipate a much more hawkish stance from the central bank due to persistent, sticky inflation readings. Higher interest rates directly reduce the appeal of non-yielding assets and force capital into safer government bonds. Policymakers will likely maintain elevated borrowing costs for a significantly longer period to combat the stubborn price increases across the real economy.

Traders eagerly await the upcoming Federal Reserve meeting scheduled for October 27 and October 28. The central bank committee will evaluate these fresh inflation metrics before making its final policy decision. Traditional equity exchanges and digital assets now move in near-perfect lockstep during major macroeconomic events. Bitcoin currently has a high correlation of 96.6 per cent with the S&P 500 index.

The total cryptocurrency space exhibits an even stronger 97 per cent correlation with the broader stock market. Professional traders increasingly view digital assets as high-beta extensions of traditional technology equities. This tight mathematical relationship means any negative macroeconomic news instantly pressures token prices and forces algorithmic selling.

Also Read: Could a hot inflation print send Bitcoin and Ethereum into freefall?

Technical indicators confirm the overwhelming bearish momentum across the entire digital asset landscape. Bitcoin recently broke below a very tight trading range and violated crucial historical support levels. Analysts previously identified the US$81,900-US$82,000 price range as a vital floor for the leading digital asset. The absolute failure to hold this specific area accelerated the downward price movement and triggered automated sell orders.

The 50-day moving average currently sits at US$82,636 and now acts as formidable overhead resistance for any attempted relief rallies. Aggressive short sellers completely control the immediate trend and actively push the price toward lower liquidity pools.

If the asset fails to reclaim the broken support zone in the coming days, aggressive sellers will likely drive the price down to test the US$78,000 mark. A deeper, more painful correction could easily push the valuation into the US$78,000 to US$79,250 support range, where long-term buyers might finally step in to accumulate more coins.

Excessive leverage in the complex derivatives market significantly amplified the recent drop in spot prices. The sudden macroeconomic shift forced the rapid liquidation of heavily leveraged trading positions across multiple global exchanges. Crypto derivatives platforms processed over US$71.16 million in Bitcoin liquidations within a single 24-hour period.

This massive forced selling represents a staggering 52.33 per cent increase from the previous trading session. Bullish traders absorbed the vast majority of this financial pain, as long liquidations specifically totaled US$59.15 million. These forced margin closures created a violent cascading effect that pushed the spot price even lower in a very short timeframe.

The derivatives sector currently shows a positive funding rate of +0.0048 per cent. This metric indicates that optimistic buyers still pay a premium to hold long positions despite the recent, severe price decline. Smart money traders need to see a complete stabilisation in open interest to confirm the market has successfully flushed out the excess leverage and established a firm base.

Also Read: Below US$82,620 Bitcoin could test US$81,154 next

The timing of this macroeconomic data release significantly worsened digital asset price action. The inflation report hit the trading desks during a period of notoriously thin weekend liquidity. Market makers and large institutional trading desks typically reduce their activity during Saturdays and Sundays. This lack of deep order book liquidity allows relatively small sell orders to cause massive price slippage. The combination of forced derivative liquidations and thin weekend order books created a perfect storm for volatility.

Retail traders watched their portfolios shrink rapidly as the market lacked the necessary depth to absorb aggressive selling pressure. This structural market vulnerability highlights the immense risks of holding highly leveraged positions during major macroeconomic announcements.

The total cryptocurrency capitalisation faces its own critical technical test on the daily charts. The aggregate valuation recently defended the crucial US$2.67 trillion support level against aggressive selling pressure from nervous participants looking to exit their risky bets.

Technical analysts recognise this specific price point as the exact 78.6 per cent Fibonacci retracement level from the recent major swing high. A sustained break below this crucial floor would expose the entire digital asset ecosystem to severe downside risks and panic selling. Aggressive bears would then target the yearly swing low situated precisely at US$2.6 trillion.

Conversely, a successful and decisive defense of this support zone could spark a powerful relief rally across the board. Confident buyers might then push the total market capitalisation back toward the 50 per cent Fibonacci level at US$2.77 trillion. The overall structural health of the digital asset ecosystem depends heavily on maintaining these aggregate valuation thresholds and demonstrating underlying buyer demand.

The immediate future of the cryptocurrency sector hinges entirely on the upcoming economic data releases. The Bureau of Labor Statistics will publish the highly anticipated Producer Price Index report later today, October 15, to provide further clarity on wholesale inflation trends.

Also Read: Bitcoin’s US$83,000 test: Can institutional demand hold the line?

This specific data point serves as the final major inflation metric before the central bank convenes for its crucial policy meeting. A softer reading could provide immediate relief to battered risk assets and spark a short-term technical rebound. Another hot inflation print will undoubtedly extend the current aggressive selloff and drive prices to new local lows.

I view the current market environment as a classic macro downdraft, in which stubborn inflation fears trigger a distinct flight from risk. Institutional buyers must step in to defend the US$81,000 support level and stabilise the bleeding across major trading pairs.

The trading action will soon reveal whether smart money views this dip as a prime buying opportunity or if more deleveraging awaits impatient retail traders. Traders must closely monitor daily closing prices to confirm the prevailing directional trend. A daily close below US$81,000 will confirm further downside toward lower support zones and validate the bearish thesis.

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At M360 ASEAN in Kuala Lumpur last week, the GSMA released its ASEAN Consumer Scam Report 2026. Nearly half of reported scam cases across six markets remain unresolved. Nearly nine in ten consumers said they had recognised AI being used in scams, from deepfakes to voice cloning. Julian Gorman, the GSMA’s head of Asia Pacific, summed it up: “Access alone is no longer enough.”

A month earlier, the GSMA’s Mobile Economy Asia Pacific 2026 named the rise of AI and declining digital trust as two of four forces shaping the region’s next phase. The share of ASEAN consumers who say they have been scammed rose from 31 per cent in 2024 to 45 per cent in 2025.

Across the region, operators are now moving AI into the customer app. The technology operators are racing to deploy is the same technology eroding the trust they need to deploy it. That dependency is missing from most business cases.

Trust is now a churn number

For years, trust sat in the compliance column. That is changing. The 2026 scam report found that people who have been scammed are more than twice as likely to switch accounts or providers as those who have not. The 2025 edition found that 36 per cent of consumers would switch financial services provider for stronger security built on telecom data.

Operators hold both sides of that equation. Their networks carry the calls and messages scammers rely on, and their data can help stop them. Some are already treating protection as a product: CelcomDigi sells a dedicated safety range alongside its mobile and fibre plans, and Globe has convened a Philippine anti-scam coalition with the Global Anti-Scam Alliance. Trust is already on the price list.

The risk sits with the operator

AI inside a telco app runs on the operator’s license and on a billing relationship that may be decades old. When a vendor’s model gets something wrong, the regulator calls the operator, and so does the customer.

Southeast Asia has mature rules for networks and, increasingly, for personal data. Consumer-facing AI inside an operator’s app is newer ground, and in practice much of its governance still lives in vendor contracts. That is a thin layer on which to rest a national operator’s reputation.

Also Read: Inside Singapore’s biggest telecom cyber defence operation

Subscribers have already written part of the spec

The most useful finding in the GSMA’s 2025 scam research was not a victim count. Consumer comfort with sharing data with conversational apps fell from 53 per cent to 40 per cent. In the same survey, 72 per cent said they were comfortable with limited, purpose-bound checks that use network signals to protect them.

Read together, those numbers describe the AI subscribers will accept. Not a chatbot that asks them questions. A system that works in the background, for a defined purpose, on as little personal data as possible.

Four tests operators can apply

In this context, trust is architecture, not assurance. Four design choices can be tested before anything goes live.

• First, the AI reads patterns, not people. There is no conversational surface between the subscriber and the model, so there is nothing to impersonate and nothing to phish.
• Second, the operator holds the identity key, and the intelligence layer sees only irreversible identifiers.
• Third, data is processed in-country.
• Fourth, the capability is exposed as an operator-controlled API: every call is logged, priced and revocable, and the operator can switch it off.

We had to build to these tests ourselves, because operator security teams kept asking the right question: if this sits inside our app, how do we know you will not walk off with our data? The answer that worked was not a promise. It was something their own teams could test before launch. That should be the standard for every AI vendor in this market. Do not ask for trust. Hand over something to test.

Also Read: How Agentic AI will create telecom’s first truly autonomous workforce by 2030

Breadth substitutes for intimacy

Personalisation is usually assumed to need more personal data. It does not have to. A system that sees wide behavioral signal across telecom, payments, content and gaming can personalise on patterns rather than identity. The more it sees of what people like you do, the less it needs to know about who you are.

Where trust is actually earned

Most subscribers do not decide whether to trust an operator by reading its privacy policy. They decide in small, frequent exchanges: whether points are credited when promised, whether a voucher works at the till, whether an offer feels useful or intrusive.

A user who watches food content and sees grocery vouchers surface first experiences AI as a service. The same signal used to push products they never asked for reads as surveillance. Every honored reward is a deposit. Every offer that feels like tracking is a withdrawal.

That exchange matters beyond churn. The GSMA estimates that 3.1 billion people live within mobile broadband coverage but do not use it. For many of them, the question is not whether the network reaches them. It is whether being online is safe enough to be worth it.

Operators planning AI launches for 2027 should be able to answer two questions before signing a vendor. Can our own security team test this before it goes live? Can we switch it off tomorrow? If the answer to either is no, the AI is not ready, however good the demo looks.

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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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Human rights are getting diluted or ignored in the oft-neglected “S” (social) of ESG, even though the rules for protecting them have been clear for a decade and a half.

The 2011 UN Guiding Principles on Business and Human Rights make every company responsible for respecting human rights and for finding, preventing, and repairing the harm its business causes.

In ESG terms, human rights are the basic protections owed to everyone a business affects, such as safe work, fair pay, freedom from forced labour, and a community’s say over its land. Many companies still fill the social pillar with donations, wellness programmes, and diversity campaigns, and leave out the people their operations touch most directly.

The World Benchmarking Alliance assessed the world’s 2,000 most influential companies in 2024 and found that more than 90 per cent fall short on human rights, working conditions, and ethics. Only one in five checks its supply chain partners for human rights risks, and just 4 per cent commit to paying a living wage.

I’m seeing some of the clearest answers to that failure coming from Southeast Asia, where some companies and governments are protecting the workers and communities their growth depends on, often before any law forced them to, and simply because these are inalienable rights.

Carbon’s easy to count. So what about the delivery rider in the flood?

Emissions come in tonnes, with standard units and reporting rules. A worker’s exhaustion, family’s lost land, or rider’s fear of the next flood has no standard unit, so it rarely makes it into the sustainability report, and the “E” gets most of the attention.

That imbalance falls especially hard on three groups in Southeast Asia: gig workers, communities living on land that companies want, and the people doing dangerous industrial work for the green economy.

Ride-hailing and delivery apps run on drivers/riders classed as independent contractors, outside most labour protections. Researchers from Fairwork, a project based at the University of Oxford, scored platforms in the Philippines out of 10 for fair pay, conditions, contracts, management, and representation. The best scored 3, and none could show that their workers earned the minimum wage after costs. One rider they interviewed worked an 11-hour day for PHP 750 and kept PHP 460 after fuel, phone credit, maintenance, and his vehicle loan. The platforms take a commission of at least 20 per cent on each job.

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

Those workers keep riding through heatwaves and typhoons, because the app pays only when they move. They carry the risk, repair bills, and lost income when a storm hits, while demand for deliveries goes up.

Grab began giving drivers and passengers free personal accident insurance across all six of its markets in 2016, years before any law required it. GrabCar was the only Philippine platform that Fairwork found to adequately protect its workers from the risks of the job. Singapore has since put that principle into law: Its Platform Workers Act now gives ride-hailing and delivery workers employee-level injury compensation, retirement contributions, and the right to form associations that represent them.

The right to say no to a project

Plantations, mines, and even solar and wind farms need land, and some of it belongs to Indigenous and rural communities who have farmed it for generations. International standards say those communities must give their free, prior, and informed consent before a project goes ahead, which means they also have the right to refuse.

Families lose the ground that feeds them when companies ignore that right. In 2022, P&G, Nestlé, Hershey’s, and Colgate-Palmolive suspended business with a large Indonesian palm oil producer after farmers in Central and West Sulawesi said their land had been taken and that those who objected faced criminal charges.

The clean energy transition can repeat the same harm. Only four of the 12 renewable energy companies in a Business & Human Rights Resource Centre survey of Southeast Asia had substantial human rights commitments, and only two publicly committed to respecting Indigenous Peoples’ rights. The frontrunners were ACEN in the Philippines and B.Grimm Power in Thailand. B.Grimm assesses every one of its sites and direct suppliers for human rights risks every three years, along with its joint ventures.

Green metals, human cost

Indonesia’s nickel boom feeds the batteries that power electric vehicles around the world. On Christmas Eve 2023, a furnace exploded at a smelter in the Morowali industrial park and killed 21 workers, 13 Indonesian and eight Chinese, and injured 38 more.

Also Read: Why investors and customers are betting on ESG-aligned startups

Those were 21 people who went to work and didn’t come home. Carmakers can build an electric car’s battery on that kind of loss, and no carbon disclosure will show it. Companies that take safety seriously treat it as a human rights duty across the whole supply chain, contractors included, and B.Grimm’s site and supplier assessments show one way to do it.

Why this is existential

Climate change and human rights abuses land on the same people. Excessive heat on the job affects 74.7 per cent of workers in Asia and the Pacific, above the global average of 71 per cent. Delivery riders and farmers work outdoors in that heat, and furnace crews work beside it. My concurrent work with an NGO on climate policy keeps showing me that overlap. A climate strategy that ignores these workers only moves the damage from the atmosphere onto human beings.

ESG exists to make business answer for its effects on the world, and it stops doing that job when companies reduce the “S” to charity and slogans. Southeast Asia is building the factories, data centres, plantations, and power grids it will rely on for the next half-century, and the choices companies make now will decide whether the people doing that work share in the region’s future or pay for it.

Where to start

Thailand’s Ministry of Justice is drafting a mandatory human rights and environmental due diligence law that would cover manufacturers with annual revenue above THB 500 million and service businesses above THB 300 million, with fines of up to THB 5 million. Companies that write a serious policy now will be ready for it.

While helping companies in Asia to develop their policies, the strongest I see are specific. One forward-thinking Japanese client’s applies a policy that rests on the Universal Declaration of Human Rights, ILO’s core labour principles, and UN Guiding Principles. It rules out forced labour, human trafficking, and child labour, bars discrimination and harassment, and protects the right to form unions. It also commits the company to due diligence, remedy for harm, a confidential reporting channel to its compliance committee, and training, and the policy itself is subject to revision.

Any company in the region can start the same way: Write down the risks to people that your business creates, check your own sites and suppliers against them, and give workers and communities a way to raise concerns that doesn’t run through the managers they’re complaining about.

The people who build, deliver, and grow what our economies run on belong at the centre of the “S”. Putting them back there is the least business can do, and it may decide whether this region’s growth is worth having.

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Sit in the architecture review of almost any platform that has reached four ASEAN markets and the same diagram appears. One product on top. Underneath, four data stores, four payment integrations, four know-your-customer flows (the identity checks that onboard each user), and four sets of legal entities and directors. Adding Malaysia means adding a fifth column and a fifth team.

Nobody designed it that way. Each market was added while the opportunity was live, by the fastest route to compliance. It worked four times. That is the problem.

Now the region has agreed to write some of its rules once. ASEAN’s senior economic officials said in May that negotiations on the Digital Economy Framework Agreement, DEFA, were concluded, with signing targeted for November. It covers digital trade, payments, digital identity, data governance and cross-border e-commerce. Studies cited in their statement put the region’s digital economy at about US$1 trillion by 2030 on current trends, and as much as US$2 trillion with implementation. The market’s reading is simple: the cap on cross-border digital scale just came off.

The cap did not come off, it moved inside

I read it differently. Scaling is the ability to add markets without a proportional rise in cost, complexity and founder attention. DEFA lowers the cost of entering a market from the outside. It does nothing about the cost of being rebuilt on the inside. So here is the law I keep returning to: when the outside cost of entering a market falls, scale is capped by how many times the business has to be rebuilt inside.

Walk it through. Market one costs what it costs: a data store, a payments connection, an onboarding flow, an entity. On a country-by-country build, market two costs almost the same again, because little of market one transfers. Market three costs the same again, and now someone must reconcile three versions of the customer. Cost per market stays flat while coordination load, the management attention spent keeping the copies consistent, climbs with every addition. That is not a scaling curve. It is a multiplication table.

A firm that built one operating layer sees a different curve. One data architecture, one payments abstraction, one identity and compliance model, with each country’s rules held as configuration rather than as a separate system. For that firm, shared external rules make market two cheaper, then three, then four, because the hard work was done once. The same agreement bends the curve for one company and leaves the other flat. The difference is not the treaty. It is the floor plan.

Also Read: AI boom pushes chipmakers to expand across Asia Pacific, FedEx report finds

The ceiling sits differently at each size. For a micro business it is the home market plus one existing channel, and it moves when another country’s system recognises its business identity. For an SME it is the number of lanes one person can track, and it moves only when the lanes collapse into one. For a regional operator it is the number of parallel stacks, and it moves when they are consolidated. For a nation, it is political: whether a regulator trusts that data leaving the country does not mean control leaving with it.

Corridors built in pairs

Here is the detail most people skip. We have already run a version of this experiment. By December 2025, ASEAN’s finance ministers and central bank governors reported 29 QR and person-to-person instant payment links, within the region and with outside partners. QR payments across them reached 36.2 million transactions worth US$716.4 million in 2025. That is real, and still small against domestic volumes.

Why small? My read is that each link was built as a pair. Two systems agree a standard and a corridor opens. The next corridor needs its own agreement, and every operator carries its own settlement, currency conversion and merchant onboarding for it. Shared standards made each handshake cheaper. They did not make the twentieth corridor cheaper than the second, because the work sat inside each participant, not between them.

Singapore’s Deputy Prime Minister Gan Kim Yong has said seven ASEAN countries have signed up to Project Nexus, a multilateral link meant to replace the pair-by-pair model. A shared rail bends the curve only for the company that connects to it once. Connect it to four stacks and you have built four connections.

One thing nobody can tell you yet. At the time of writing, the full text and the country-specific exceptions have not been published, so no one knows how much national rule will survive. That uncertainty is the argument for one operating layer. A firm that holds exceptions as configuration can absorb whatever remains. A firm with four stacks cannot.

Also Read: AI chats are becoming the new starting point for online shopping

Two moves, depending on your exposure

If DEFA touches your business directly, the move is architectural before it is legal. Consolidate toward one data layer, one payments abstraction and one identity model. That pulls the replicability lever, because what copies across borders is what you build once. Stop funding the fifth country team and fund the shared core, which lowers the capital needed per unit of growth. Use local partners for the last-mile exceptions instead of a full rebuild, and keep those terms fair and written down.

When one dependency sets the speed of the whole system, a partner you have squeezed becomes your slowest link. Most of the businesses that use my scaling framework are no longer asking when DEFA is signed. They are asking how many times they are carrying the same company.

If you are less exposed, a single-market firm or one whose core is not digital, use the quiet period. Hire or acquire people who understand both the regional rules and the national exceptions. That pulls talent density, because that capability will stay trapped in a few heads until someone builds it into a playbook. Then position for the harder markets, finance, health and logistics, where baseline rules plus local exceptions create a barrier that early movers can price.

India shows the other order of operations. It built one domestic instant-payments layer, UPI, for a very large home market, then connected it outward to other countries’ systems instead of rebuilding it for each. The core came first and the corridors were extensions. ASEAN’s QR links show what happens when the corridors come first.

Sign the agreement. Ratify it. Watch the exceptions. Then do the unglamorous work of making sure you only have to build the company once. Outside rules will keep getting cheaper. The inside is the only cost you set yourself.

When entry gets cheaper, scale is capped by how many times you have to be rebuilt.

—

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A lot of startup advice about predictive AI starts too early. It starts with the model, the tool, the data stack, or the dashboard. Those things matter, but they are not the first question.

The first question is simpler: what decision should this prediction change? If a startup cannot answer that clearly, it is usually too early to talk about predictive AI.

This sounds obvious, but it is where many teams drift. They say they want to predict churn, forecast demand, score leads, or spot revenue risk. Those are reasonable ideas. But a prediction by itself does not help much. It only becomes useful when someone knows what should happen next.

Take churn as an example. If a model flags that a customer may be at risk of leaving, what changes? Does someone from customer success reach out? Does the account get reviewed? Does the product team check whether activation is stuck? Does the system trigger a reminder? Or does the prediction sit in a dashboard where nobody acts on it?

That is the real test.

The same problem shows up in sales. A startup may say it wants to predict which leads are most likely to convert. Fine. But what decision changes after that? Do those leads get faster follow-up? Do they get routed to a more experienced salesperson? Do they receive different onboarding or pricing information? If the workflow stays the same, the prediction is just another score that people may ignore.

This is why defining the decision first is a useful habit. It forces the team to think in workflow terms, not just data terms.

Also Read: Why SEA’s retailers need to protect AI-powered customer experience with observability 

A practical way to do this is to write the decision as one sentence.

• If this prediction is high, we will do X.
• If this prediction is low, we will do Y.
• If the model is uncertain, we will ask a human to review it.

That simple exercise clears up a lot. Sometimes it shows the startup is not ready for prediction yet. Sometimes the team realises it does not need a model at all. A basic rule or threshold may be enough. Sometimes it reveals that the real problem is not prediction, but missing process. The company may not even have a clear follow-up path once a risky account or warm lead is identified.

This also helps with data collection. Once the decision is clear, the useful data becomes clearer too. If the goal is to predict which trial users are likely to activate, the team should care about setup behaviour, time-to-first-action, drop-off points, and usage patterns. If the goal is to predict which inbound leads deserve faster follow-up, the team should care about source, page visits, company profile, response speed, and prior engagement. Without the decision, teams often collect too much and still learn too little.

Also Read: AI boom pushes chipmakers to expand across Asia Pacific, FedEx report finds

The same rule helps with pilots. A startup does not need to roll out a grand predictive AI system on day one. It can start with one narrow decision that already matters. Which users should get a check-in email? Which leads should be escalated first? Which accounts should be reviewed before renewal? A smaller pilot is easier to judge because the team can see whether the decision actually improved.

This matters because prediction sounds smart even when it is not useful. A chart, a score, or a risk label can create the feeling that the company has become data-driven. But if nobody can explain what the business does differently because of that prediction, then the model may be adding complexity without adding leverage.

So before asking which tool to buy, which model to train, or which platform to use, a startup should pause and ask one operational question: what decision should this prediction change?

That question does not make predictive AI less ambitious. It makes it more usable. And for a small team, usable usually matters more than impressive.

—

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.

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Thirty-seven per cent. Eleven per cent. Those two numbers, tucked deep inside Jobstreet by SEEK’s Singapore Workplace Culture Study 2026, may say more about the region’s productivity problem than any headline about AI or inflation.

The first is the share of employees who try to manage workplace pressure by quietly drawing their own boundaries: muting the group chat, blocking the calendar, putting the phone face down after dinner. The second is the share who feel comfortable raising that pressure with their manager.

Also Read: SEA’s CEOs want innovation, but employees feel stuck in execution mode

In other words, Singapore’s workers are negotiating their working hours with themselves. The one person who could actually change the terms is rarely part of the conversation.

The study, which polled 600 employees and jobseekers and 200 hiring decision-makers, has a headline finding that is already well documented: bosses say they want initiative, workers try to prove themselves through availability.

Some 49 per cent of employers rank proactive problem-solving as their top expectation, yet only 32 per cent of employees prioritise it. Meanwhile, 63 per cent of employees lean on rapid replies and 52 per cent on after-hours availability, behaviours just 39 per cent and 18 per cent of employers respectively expect.

The harder question is why the mismatch survives when both sides can see it. Some 85 per cent of employers concede their organisations expect staff to stay reachable, and 81 per cent of those acknowledge the long-term risks, chiefly burnout. Read across the data, the answer keeps landing on the same layer of the org chart: the line manager.

The signal nobody sends

Hybrid work removed the oldest, crudest performance metric of all: being seen at your desk. Nothing replaced it. In that vacuum, employees reach for what is legible. A reply at 11pm is visible. Three hours spent untangling a broken process is not.

The report calls this visibility bias, and leadership is part of the feedback loop. Among employees under always-on pressure, 35 per cent blame explicit manager or leadership expectations, a view echoed by 41 per cent of employers who point at senior leadership. And 46 per cent on both sides describe constant availability as an unwritten cultural baseline.

“Unwritten” is the operative word. Nobody instructs staff to answer at midnight. Nobody tells them not to, either. In workplaces across Southeast Asia, where hierarchy still shapes who speaks first in a meeting, asking a boss whether a message really needs an answer tonight can feel uncomfortably close to insubordination. So people don’t ask, and the 11 per cent figure starts to make sense.

The youngest are paying the most

The burden is not evenly spread. Some 64 per cent of workers aged 25 to 34 face frequent always-on pressure, and much of it is self-generated: 33 per cent of this cohort cite self-imposed pressure, compared with 21 per cent of those aged 35 to 44 and 16 per cent of those aged 45 to 54. Career-progression anxiety follows the same curve, at 35 per cent for early-career workers against 27 per cent for the oldest group surveyed.

Also Read: Gen Z isn’t hard to manage, you just need to rethink how you lead

Older employees feel the pressure almost as often (62 per cent for ages 35 to 44, 61 per cent for 45 to 54) but for different reasons: structural workload and embedded culture rather than fear. Put crudely, the young are performing, while the middle-aged are carrying.

The cruel part is that the performance does not pay. Some 62 per cent of employees say always-on behaviour has had no impact on their career progression, and only 6 per cent believe it is how people actually get ahead. Young workers keep doing it anyway, which is less a sign of irrationality than of the absence of any clearer signal. For companies still rethinking early-career hiring for a remote world, that silence is a design flaw, not a personality trait.

Support that never reaches the desk

Employers will argue they have tools in place. The data suggests those tools rarely survive the journey from the HR handbook to the individual worker. Half of employees say they receive no support to manage workplace pressure, while only 22 per cent of employers believe they offer none. Workload conversations are offered by 40 per cent of employers but experienced by 22 per cent of employees. Boundary protections show a 36 per cent to 19 per cent split, and confidential feedback channels a 33 per cent to 13 per cent split.

Notice what those programmes have in common. Almost every one of them depends on a manager to start the conversation, honour the boundary or act on the feedback. Yet manager development sits near the bottom of employers’ investment plans at 12 per cent, alongside workload-management improvements at 13 per cent. AI and digital skills (34 per cent) and technology tools (25 per cent) lead the list.

AI could widen the gap

That ordering carries a real risk. Some 24 per cent of employers are restructuring towards leaner teams, and 40 per cent already admit workloads cannot be finished within standard hours. Layer faster AI-driven workflows on top, without teaching managers to ration capacity, and the likely result is more tasks arriving more quickly, not fewer late-night messages.

As one contributor recently argued, AI will change what work has to be managed; it does not remove the need to manage it. If anything, it may expose weak leadership faster than any engagement survey.

What a useful manager would do

The report’s prescriptions are sensible, if a little consultancy-tidy. Strip response speed and digital status indicators out of appraisals. Write down what genuinely counts as urgent. Run weekly capacity reviews in which managers and teams agree what to prioritise, what to delay and what to stop altogether. It even supplies a script for employees to propose their priorities and agree offline hours in advance.

The weak spot is authority. A “stop” list only works if the manager drawing it up is allowed to stop anything. Many middle managers in lean regional startups are themselves squeezed between founders chasing growth and teams drowning in status updates, and the skills a monitored workplace demands are rarely the ones they were promoted for.

Also Read: AI can increase productivity. It can’t increase human capacity

The usual caveats apply. This is a Singapore-only sample, employer responses are self-reported, and a jobs platform benefits when workers feel restless. Still, 55 per cent of employees and 45 per cent of employers agree that both sides share responsibility for always-on culture, which at least gives them a starting point.

For founders, the cost of ignoring it is measurable: 36 per cent of employees are already exploring other jobs, and across the region keeping good people is proving harder than hiring them. The gap between 37 per cent and 11 per cent is, in the end, just one conversation. Someone senior needs to be the one who starts it.

The post Employers are buying AI tools. Their managers are being left behind appeared first on e27.

Almost every founder I meet reaches the same conclusion at roughly the same moment. You cannot outbid a funded competitor on salary, so you give people a stake in the outcome instead. The logic is sound. Equity turns salary-limited hires into owners and aligns the people doing the work with the value they create.

Then the plan gets written. Usually quickly, usually from a template, usually at the point the company is 10 people in one city and the founder is doing it between two other things. That is the version that survives, unexamined, for the next four years.

What changes underneath it is the company. The first engineer in Bengaluru. The first salesperson in Sydney. A designer who moves to Lisbon and keeps working. Cross-border hiring is now normal inside the first 10 hires, and the plan that was written for one country is quietly doing work it was never designed for.

It is a stack of decisions, not one

What looks like a single call, let us give the team equity, is really a series of choices that each carry legal, tax and reporting consequences.

You are choosing between options and actual shares. You are deciding whether phantom units, which track share value and pay out in cash without handing over ownership, are a better fit. You are setting vesting, and sometimes reverse vesting for founders and early joiners. You are agreeing reserved rights, picking a valuation method, and inheriting statutory reporting obligations in every country where you have someone on the plan.

None of it sits in isolation. Your valuation method affects the tax your employee pays. Your vesting structure affects when that tax is triggered. A reporting rule in a country you hired into once creates an obligation nobody in the company knows exists. The complexity compounds, and it compounds fastest exactly when you are growing.

Two colleagues, the same grant, different outcomes

Where your company is incorporated sets your compliance baseline. A US-incorporated company generally has to value its shares regularly, often annually, to price options defensibly. Every fresh valuation moves the strike price, which changes what your team believes their options are worth. If the numbers climb faster than expectations, the incentive dulls even as the company succeeds.

Also Read: Rethinking ESOP pools in India: Building ownership without losing control

Where your employees sit matters just as much. Some countries put the reporting obligation on the employer, others on the employee. The taxable moment moves as well. It can attach at vesting, at exercise, or only at an eventual exit. Two people with identical grants, doing identical jobs, can end up with materially different outcomes because one of them lives somewhere else.

Singapore is a good example for anyone hiring here. Employers carry reporting obligations on share plan gains, and a foreign employee who ceases Singapore employment can be treated as having exercised outstanding options before they leave. That is a tax charge on a gain they have not received in cash, arriving at the worst possible moment in someone’s working life.

Your team does not even want the same thing. Many people prefer to pay nothing until an exit gives them the cash to pay with. Others would rather pay a small amount now, while the shares are cheap, to shrink a much larger bill later. Both are rational. A good plan makes room for both where the law allows, and most templates make room for neither.

The part that costs you money

Here is the part founders underweight. Institutional investors read your cap table and your incentive plan closely, and they read them together. An overly complex or poorly documented plan is a diligence problem before it is anything else. Unclear ownership. Unquantified tax exposure. Reporting nobody can produce on request. Each of those slows a round, and a slow round is a cheaper round.

Also Read: The best new year resolutions for startup founders: Offering ESOPs that actually work

A clean plan does the opposite. It signals a company that is straightforward to invest in and, later, straightforward to acquire. That is not a compliance benefit. That is a valuation one.

What founders do about it

When a plan has to serve people in several countries, structure starts earning its keep. Some founders hold shares in trust for the team, which keeps the cap table tidy while preserving employees’ economic interest in the upside. Others set up a dedicated special purpose vehicle that holds equity for employees as a group, centralising the reporting and giving everyone a consistent experience regardless of where they log in from.

Neither is free. Each has trade-offs in cost, control and tax treatment, and the right answer depends on where your company sits and where your people do.

Decide early, revisit deliberately

The through-line is that these choices are far cheaper to make well at the start than to unwind later. A plan designed around your entity’s rules, your team’s locations and your investors’ expectations stays effective as you grow. One assembled piecemeal loses its pull precisely when you need it to hold.

The question worth asking is not whether your ESOP is generous. It is whether it still describes the company you have actually become.

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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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