Day: October 11, 2026

There’s a fundamental question that most leaders are avoiding right now, not because it’s too hard, but because the answer is uncomfortable.

If you don’t understand where scarcity lives in the current economy, and you keep focusing on supplying more into markets with less demand or that are already overcrowded, you’ve forgotten Economics 101.

Scarcity drives value. That’s not a theory, it’s the foundation. When an item is scarce, it commands a higher price as its value is derived from its rarity. As supply falls short of demand, consumers are willing to pay more to acquire it. When you flood supply into a saturated market, you don’t create value. You erode it. Price compression. Commoditisation. A race to the bottom that nobody wins.

So the question every leader should be asking isn’t what can we build? It’s where is the genuine scarcity?

Technology is becoming abundant. People aren’t

The numbers are striking. The cost of machine intelligence has fallen by roughly 1,000x in just three years, from US$60 per million tokens in 2021 to US$0.06 with Meta’s Llama 3.2. Intelligence, once one of humanity’s most scarce resources, is becoming ubiquitous, abundant, and essentially free.

AI tokens have seen steeper cost reductions than streaming services and legacy technologies combined, driven by advancements in computational efficiency and competitive market forces. Everyone has access. The barriers are collapsing fast. Technology, for all its power, is no longer the scarce resource.

What’s actually scarce right now? According to Deloitte’s 2026 Global Human Capital Trends report, demographic shifts and disappearing workforces are making human capacity itself a scarce resource, elevating the need to invest where humans create unique and irreplaceable value.

Trust. Judgement. Relationships. And yet most leaders are doubling down on the tool, not the gap.

Audit point one: Where are you adding supply, and is there still demand?

Before you ship the next feature, launch the next campaign, or hire for the next role, map it. Is the market you’re entering hungry or saturated?

The data on tech saturation is clear. While AI spending reached US$200 billion in 2024, nearly 65 per cent of organisations report their AI initiatives have fallen short of expectations. And according to Gartner, 30 per cent of generative AI projects will be abandoned after proof of concept by end of 2025, not because the pilots failed technically, but because the human and organisational foundations weren’t built.

Most leaders can answer the technology question cold. Almost none can answer the demand question with real data. If you can’t point to a specific, unmet human need, you’re not leading. You’re just producing.

Also Read: Singapore’s AI dividend will depend on what happens after the pilot phase

The trap most leaders fall into

Tech-first leaders ask: what can this do? People-first leaders ask: what do people actually need?

One is supply-side thinking. The other is strategy.

To truly thrive with AI, leaders must recognise that its power lies in augmenting, not replacing, human connection. The creativity, emotional intelligence, and judgement that only humans can provide will always be in demand.

The leaders who consistently win are the ones who start with where people are underserved, frustrated, or stuck, and then decide whether technology is even the right answer. They’re reading the map before they start driving. Most leaders right now are driving fast, with no map, and calling it innovation.

Audit point two: What would break if you removed the tech?

This is the revealing question. Strip out the AI, the platform, the automation. Does the value disappear, or does it hold?

If the answer is it disappears, you don’t have a leadership strategy. You have a dependency.

Real value lives in the human layer. Trust is the foundation of most successful organisations. In high-trust organisations, employees feel safe to take risks, express themselves freely, and innovate. In contrast, employees at low-trust organisations are often bogged down by office politics and infighting, more likely to withhold information and hoard resources because they don’t feel safe sharing them.

If your entire proposition collapses the moment the tool goes away, you haven’t built anything. You’ve borrowed someone else’s infrastructure and called it a business.

Also Read: Why Singapore firms fear data sovereignty failures but remain underprepared

Direction over efficiency

There’s a version of tech adoption that looks like progress but isn’t. Only 48 per cent of AI projects make it into production, according to Gartner research, meaning more than half of all AI initiatives die somewhere between proof-of-concept and actual deployment. The research is consistent: this isn’t a technology problem. It’s a direction problem.

Efficiency without direction is just faster mediocrity. You can automate your way through an entire quarter and end up further from where you needed to be, just with better dashboards to prove it.

Audit point three: Are you solving for scarcity or comfort?

Most tech adoption happens because it feels easier, not because it closes a genuine gap. The proof is in the trust data: globally, 68 per cent of respondents say they are worried that business leaders purposely mislead people, a 12-point increase from 2021. Most leaders are starting with a trust deficit and will have to earn trust with consistent communication and transparency.

Ask yourself honestly: am I using this tool because it moves me toward something scarce and valuable, or because it feels like progress?

People worldwide seek hope and trust as foundational leadership traits. Understanding these needs is what helps leaders build relationships and inspire others through changes in the world of work. The leaders who can anchor to that, and act on it, are the ones building where others aren’t looking.

The audit

Run these three questions quarterly. Not annually. Not when things slow down. Quarterly.

Where are you adding supply, and is there still demand? What would break if you removed the tech? Are you solving for scarcity or comfort?

Demand. Dependency. Direction.

That’s the audit. And right now, most leaders are failing it, not because they lack intelligence, but because they’re leading with the tool instead of leading with the people the tool is supposed to serve.

The map comes first. Always.

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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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DePIN (Decentralised Physical Infrastructure Networks) crowdsources real-world resources like compute, storage, and connectivity via blockchain incentives. RWA tokenisation brings tangible assets (real estate, commodities, infrastructure) on-chain for fractional ownership, liquidity, and global access. Together, they address SEA’s challenges: high infrastructure costs, limited banking reach, and volatility in local currencies.

Global tokenised RWA (excluding stablecoins) surpassed US$36 billion by late 2025, with forecasts reaching trillions by 2030. DePIN adds real utility: devices generate verifiable data or yield that becomes tokenised RWA.

Global alliances driving convergence

Western alliances set benchmarks. The Superintelligence Alliance (ASI, merging Fetch.ai, SingularityNET, Ocean Protocol) focuses on decentralised AI but connects to RWA through partnerships like SuperWorld. This integrates ASI’s AI stack with virtual real estate, enabling monetisation of physical-digital interactions via tokenised assets, DePIN hardware, and RWA rewards, democratising access to infrastructure yields.

These alliances standardise protocols, attract capital, and reduce fragmentation, turning AI-DePIN data into investable RWA.

SEA’s regulatory momentum and emerging alliances

Vietnam’s 2026 framework marks a shift. The Law on Digital Technology Industry (effective January 1, 2026) recognises digital assets, backed by real-world underlying (excluding securities/fiat). Resolution 05/2025/NQ-CP launches a five-year pilot for issuance/trading, emphasising compliance, cybersecurity, and tokenised assets for practical use.

This creates opportunity for RWA in infrastructure, real estate, and green assets. SEA builders leverage it with alliances tailored to local needs: mobile onboarding, affordable hardware, cross-border utility.

Also Read: Bitcoin dominance at 58.5% and the 55% line that still blocks altseason

Examples include Depin Alliance (depinalliance.xyz), uniting DePIN builders for standards and adoption. It hosts events like the Yacht Party in Ho Chi Minh City, a luxurious networking gathering with global VCs, DePIN leaders (IoTeX, Aethir, Witness Chain), and founders, fostering collaborations on RWA convergence.

Insights from leading alliances in Asia

I was previously CMO at U2U Network, a DePIN-focused Layer-1, which was involved in organising Depin Alliance conferences and side events across Asia. From that position, onboarding and fragmentation were the problems that came up most often when builders, investors and regulators met at these events. Alliances in this space tend to be judged on whether they reduce those two frictions, so it is worth asking what they have actually changed for members.

Niche approaches like “opening association as a service” (templates, events, support for sector alliances) help scale fast. In SEA, where RWA attention grows (tokenised commodities, private credit), alliances capture momentum by linking physical networks to liquid markets.

Challenges and path forward

Fragmentation across chains causes pricing gaps. Hardware costs and volatility deter contributors. Regulation varies (Vietnam pilots cautiously, Indonesia securities oversight) but clarity boosts confidence.

In 2026, alliances will mature DePIN-RWA. Global ones provide standards; SEA ones localise for inclusion. The convergence promises practical benefits: tokenised infrastructure shares, verifiable yields from physical devices. Builders who connect communities early will lead the wave.

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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.

The post DePIN and RWA alliances in 2026: Global momentum meets SEA opportunities appeared first on e27.

Jenni is an academic research and writing platform used by more than six million researchers, who have written over 15 million papers on it. The US-headquartered company says it passed US$10 million in annual recurring revenue (ARR) this year and is profitable, having grown almost entirely from revenue after raising only a small angel round.

While ChatGPT remains the first port of call for many researchers, Jenni has deliberately stayed narrow, focusing specifically on academic work. Its citations link to real papers rather than invented ones, users can trace every AI-generated claim back to the source documents behind it, and its AI Declaration feature, built for researchers submitting to publishers, lets them disclose exactly how AI was used in a paper.

Also Read: India is showing the world what AI will do to jobs and education

The platform can also work directly from a researcher’s own uploaded sources, which is crucial for local or non-English research, where general AI tools still default heavily to English-language results.

In Asia, Jenni has growing university partnerships and user bases across Singapore, Malaysia, India, Thailand and the Philippines, with China emerging as its most ambitious, and most complex, expansion target.

Justin Wong, Head of Commercial Operations, helped take Jenni from US$1 million to US$10 million ARR and now leads its B2B sales and international expansion. He spoke to e27 about the decisions that shaped that journey.

A bet on careers, not semesters

For Wong, there was no single dramatic inflection point in Jenni’s growth from US$1 million to US$10 million ARR. The change was a strategic repositioning, away from general writers and students and towards higher education and academic researchers.

The logic was straightforward. A student might use a writing tool for a single semester before moving on; a researcher writes papers across an entire career. Once Jenni reoriented itself around that longer relationship, building a product a researcher could rely on “from their first manuscript to their fiftieth”, retention, willingness to pay and word-of-mouth referrals inside academic labs and departments all improved.

That shift also reshaped operations. Once Jenni crossed the US$10 million threshold, the company went international“with intent”, localising for markets across Asia, Latin America, the Middle East and Europe. At the same time it built an institutional sales motion alongside its self-serve consumer business, largely because faculty were already bringing Jenni into their departments on their own.

Also Read: Why Dropbox refuses to pick a side in the ChatGPT-Claude-Gemini fight

The bootstrapped path wasn’t originally a strategic choice; it became one. Jenni was one of the earlier players in AI-assisted academic writing, and revenue arrived early enough that raising a large venture round never felt necessary. That came with trade-offs. Every acquisition channel had to pay for itself, the team stayed lean with employees wearing multiple hats, and growth was never bought beyond what the business could sustain. The upside, Wong argues, was strategic independence. Without investor pressure to chase whichever segment was commercially hot, Jenni could commit fully to serving researchers over the long term.

Competing with the tab already open

Every researcher already has ChatGPT open somewhere, and Wong is candid that most conversations with universities start from that reality. The pitch isn’t that general models are useless. It’s that they invent citations, produce unsupported claims and generate prose with no identifiable source.

Jenni’s workflow is built to close those gaps:

• AskJenni lets researchers interrogate both their own uploaded PDFs and more than 250 million external papers.
• Autocomplete suggests one sentence at a time rather than generating full passages, keeping the researcher in control.
• Citations link to real sources across more than 10,000 citation styles.
• A four-layer review checks peer-review readiness, claim confidence, proofreading and tone before submission.

Jenni also does not train on user data in any feature.

Institutionally, Jenni avoids asking for a campus-wide commitment upfront. Instead, a department or lab runs a one-to-two-month pilot, tracking hours saved and manuscript activity, and the deployment expands based on the evidence. India-based Lovely Professional University’s pilot with more than 60 faculty members reported over 375 hours saved; Chulalongkorn University’s six-department rollout reached more than 300 users and over 750 hours saved.

Also Read: The localisation gap: Why multilingual AI isn’t enough for APAC markets

Asked what actually closes institutional deals, Wong points to Jenni’s Reviews feature. It scans a full paper for contradicted, unverified or misrepresented claims, whether a human or AI wrote them, and suggests peer-reviewed references to support them.

He, however, is careful not to reduce Jenni to a citation tool. Many users describe it, he says, as “a combination of ChatGPT, Microsoft Word, and Zotero in a single tab”: one workspace for organising sources, drafting and reviewing. That breadth, not any single feature, is what ultimately sells.

Publishers, not a publisher partnership

Wong is quick to correct a common misreading. Jenni didn’t build a feature with publishers. It built its AI Declaration feature for researchers submitting to publishers and journals, as a simple way to disclose exactly how AI was used in a paper, whether for citations, drafting or grammar correction. The declaration has now appeared in over 100 published papers, including work published by Nature, Elsevier, IEEE and MDPI. As journals formalise their AI disclosure policies, Wong sees this as part of making AI-assisted research broadly acceptable rather than suspect.

Localisation, and the China exception

Across Southeast Asia and India, Jenni’s self-serve-then-pilot motion travels well. China is the outlier. Traditional marketing and sales channels don’t work there. Researchers live on Xiaohongshu, WeChat and Douyin, behind entirely different infrastructure, which makes China the most heavily localised market Jenni has entered.

The payoff, Wong argues, justifies the effort: China publishes more research papers than any other country, and many of its universities lead global research output.

Language matters beyond China, too. Because Jenni grounds its AI in a researcher’s own uploaded sources, including local studies, theses and reports a general model wouldn’t surface, it can support hyper-local work in ways general chatbots cannot, since those often default to English-language searches. Jenni supports drafting in more than 30 languages.

This localisation sits alongside two sales cycles that feed each other. Individual researchers buy through free, monthly and annual plans; this is the self-serve business that passed US$10 million ARR in 2026. Institutions follow the slower pilot-then-expand model. Self-serve adoption is often how Jenni gets into an institution in the first place, because faculty and students are frequently already using it before a university formally engages. As universities increasingly centralise AI purchasing decisions, that dynamic turns individual subscriptions into campus-wide deployments.

The moat against bigger players

What stops a well-funded general AI company from simply adding academic features? Wong frames the defence around incentives rather than functionality. General tools optimise for fast, fluent answers sourced from anywhere on the internet, including unverifiable sources. Academic work demands citations a researcher can defend in front of reviewers. Jenni’s workflow is built around accountability, not just output speed: autocomplete, verifiable citations, four-layer review, no training on user data, and AI declarations. “Adding a citation button to a chatbot,” Wong says, “doesn’t replace that workflow.”

Also Read: The EU called ChatGPT a search engine. SEA’s AI startups should worry about what comes next

With 15 million papers written and more than six million researchers on the platform, Wong believes Jenni’s next phase lies in depth rather than breadth: owning more of the research lifecycle rather than chasing new user segments. “If we’re the tool a researcher trusts for their whole career,” he says, “the new segments follow.”

The post Why Jenni thinks researchers need more than ChatGPT for academic writing appeared first on e27.

The post AI labs are chasing a slice of the corporate pie in Southeast Asia appeared first on e27.

Across 231 Canadian publishers, the outlets that accept restricted categories charge less than half as much for ordinary articles. That discount measures who has already left the room, and founders in crypto, lending and gaming are shopping in that half without knowing it.

The cheapest quote in a media plan is usually the one worth worrying about. Founders in regulated categories learn the price side of that quickly, because the numbers arrive in an email. The access side takes a year, and by then the budget is spent.

This month ESBO Ltd, the link building and digital PR agency I run, published Canadian link building prices in 2026, built from 231 Canadian publishers filtered to a Domain Rating of 30 and above with at least 2,000 monthly organic visits. Canada is a test case here rather than a market most readers buy in. It legalised two major categories and then restricted how they may be promoted, which makes the effect on media access easy to measure. The median publisher quotes US$810 for a sponsored article. The interesting part is who quotes it.

Two markets wearing one label

Of those 231 publishers, 49 per cent quote a price for at least one restricted category. The remaining 51 per cent quote none at all.

Those two halves also price ordinary, unrestricted articles differently. Publishers that accept something quote a median US$610. Publishers that accept nothing quote US$1,110. Same product, 1.82 times the price, and the gap holds across every category in the file.

So a founder in a restricted category is not getting a discount on the market. They are shopping in a different, cheaper market, and the cheapness is the signal.

Count readers, not publishers

Site counts make the restricted market look workable. Roughly 41 per cent of Canadian publishers accept gambling content.

Now count audience instead. Add up the monthly Canadian visits behind the publishers that accept each category, as a share of all Canadian audience in the file, and gambling reaches 24 per cent of it. Cannabis reaches 34 per cent. Adult reaches 22 per cent. Anything restricted at all reaches 48 per cent.

Also Read: The most expensive links aren’t really links: What 16,625 publisher price lists tell SEA startups

That is the number to plan against. The cheap gambling placement at a median US$355 is not a bargain. It is the price of what remains after three quarters of the audience has removed itself from your list.

The line is compliance, not taste

Compare the categories and the pattern is not about respectability.

Gambling and cannabis produce the widest gaps between accepters and refusers, at 4.27 and 4.11 times. Both are legal in Canada and both carry strict rules on how they may be promoted: the Cannabis Act prohibited promotion capable of appealing to young people when it took effect in 2018, and Ontario’s regulator has since barred athletes from iGaming advertising altogether. Crypto and forex, which carry no comparable promotion rules, produce the narrowest gap at 1.51 times.

The dividing line is how much compliance exposure a publisher takes on by carrying you. A publication with written advertising standards, a disclosure policy and a named person who approves advertisers excludes legally restricted categories as a class, because each one creates obligations somebody has to own. A publication with none of that takes everything.

Disclosure behaviour confirms it from the other side. Stated sponsorship labelling runs from 11 per cent of publishers at the bottom of the authority range to 64 per cent at the top, and publishers that label charge US$1,420 against US$405 for those that do not. Formal terms, disclosure and a closed door to restricted niches travel together, and they travel with a higher price.

Why this matters in Southeast Asia

A category does not need to be illegal to lose its media access. It needs rules that make carrying it a liability.

That mechanism is already here. In January 2022 MAS issued guidelines stating that digital payment token providers should not promote their services to the general public in Singapore, including through third-party websites and social media influencers, leaving them their own sites, apps and official accounts. Whatever you think of the policy, look at what it does to a marketing plan: the third-party channels come off the table, and the startup is left talking to people who already found it.

Also Read: Startups keep scaling ops before they scale data — Here’s why it backfires

Given how much of this region’s startup activity sits in digital assets, lending, remittances and gaming, a large number of founders here are operating in the restricted half of their media market and have priced it as a bargain. This sharpens the argument I made in this column in June about needing public proof before you scale. When the channels that would carry your proof are closed to your category, the ones that remain have to be earned rather than bought.

Four adjustments

Count audience reach, not publisher counts, before approving any plan in a restricted category.

Stop negotiating the cheap half. Publishers that accept everything quote US$260 to US$390 for standard content. They are already at the floor, and there is nothing to win there.

Put the effort into the publishers who currently refuse. That is an editorial conversation about what the content says, which claims it makes and how it is labelled, not a rate conversation.

Read disclosure as quality rather than cost. The publishers that label charge three and a half times more, reach more readers, and have somebody inside whose job is to vet advertisers.

The instinct when a category gets expensive is to shop for a better price. In regulated categories, price was never the constraint. Access was.

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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.

The post Half the market will not take your money: The access problem for regulated startups appeared first on e27.