About a year ago, I started noticing a pattern in how clients were briefing us. They don’t just come to us for deliverables anymore. They were coming with questions. Which AI tools should we be using? Should we be using them at all? What does a responsible AI-assisted creative process even look like?

I spent seven years in government service before founding CreativesAtWork 14 years ago. Across those two very different worlds, I have sat across from a lot of different kinds of clients. But this was new. They were not just buying a service. They were looking for someone to think alongside them — someone who understood both the craft and the tooling, and could help them make sense of a landscape moving faster than anyone expected.

That shift told me something important: the gap isn’t just between AI and creatives. It’s between what the market now expects and what our talent pipeline is actually producing.

What clients are actually asking for now

The ask has quietly changed. Clients today don’t just want execution. They want judgment. They want someone who can look at an AI-generated concept and tell them honestly whether it’s on-brand, culturally appropriate, or legally murky. They want a creative partner who knows when to use the tools and, just as importantly, when not to.

That is a fundamentally different profile from what most creative training — formal or informal — has historically built toward.

And yet the conversation in most education and training circles is still centred on outputs: learn the tools, build the portfolio, get the brief done. The advisory layer — the ability to help a client think through AI adoption in their own creative process — is barely on the curriculum and it should be.

Also Read: What to actually prioritise when your board wants AI and everything feels urgent

Where the pipeline is falling short

Across Southeast Asia’s creative economy, which includes a large and growing freelance workforce, this mismatch is structural. Universities producing design, communications, and media graduates are still largely running programmes built for a pre-generative-AI world.

Bootcamps have moved faster, but they are largely targeting engineers, marketers and tools-specific and focused. Few focused on the mindset shift and the design thinking process together with the client.

The freelance community is largely left to self-educate. Platforms like Coursera and LinkedIn Learning have added GenAI modules, but most of what exists treats AI as a feature to understand rather than a professional context to navigate.

What actually would help

Peer-led communities are filling the gap faster than institutions. Regional design communities, Discord servers, and creative Slack groups are where working freelancers are sharing real workflows, catching mistakes, and pressure-testing AI output against live briefs. The learning is messy and informal, but it is grounded in actual work, which is more than most formal programmes can say.

Agencies that have restructured around AI are also quietly becoming training grounds. When the workflow changes, junior people inside it develop judgment faster — because they are doing real work, not doing exercises. That will be increasingly important, and how a good creative training will work. The tools are new. The apprenticeship logic isn’t.

The structural barriers are slowing progress

Three things are holding this back at scale.

Curriculum cycles move too slowly. Degree programme updates will take a while. GenAI capabilities are shifting every six to twelve months. It needs to catch up faster.

Assessment needs to be caught up as well. It is still hard to tell whether someone can genuinely work well with AI or is just using it to cut corners. Without clearer signals of quality, the market will not be able to reward real capability development the way it should.

Also Read: Think with AI: The new skill for social entrepreneurs

And the freelance workforce has no institutional home. Salaried workers access employer-sponsored training. Freelancers largely is on their own. In Singapore, SkillsFuture credits help, but take-up among creative freelancers stays low — partly because the available courses aren’t yet matched to how creative AI work actually operates day to day.

What needs to change

I would think an actionable next step for creatives is to have an industry body to lead the development of a portable, competency-based credential for AI-assisted creative work — built with practising freelancers, not just academics. One that assesses judgment and workflow design, not just tool familiarity.

For business leaders, rewrite your briefs. If you need someone who can direct AI-assisted production and advise on which tools fit the project, say that clearly. Vague calls for “AI-literate creatives” produce vague results.

And for educators: spend time with freelancers who are actually doing this work. The curriculum you need already exists in practice. It just hasn’t been written down yet.

The creative economy has always run on a mix of formal training, peer learning, and hard-won experience. What’s changed is how fast the ground is moving underneath all three.

Twenty-one years across two very different sectors has taught me one thing consistently: the people who stay relevant are not the ones who master the tools best. They are the ones who never stop asking what the work is actually for. That skill no model generates. But we need to start teaching it now.

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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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Eight years ago, I made a hiring decision that didn’t follow the usual rules. In today’s AI era, where tools can already assist with much of the work that we do, that decision feels even more relevant in hindsight. I was hiring for a fast-growing fintech company. The shortlist was what you’d expect: candidates from top universities, strong academic records, and big brand internships. On paper, most looked solid.

But one candidate stood out for a very different reason. He didn’t have a strong degree. But when we gave him a problem to solve, something clicked. He broke it down clearly, asked the right questions, and explained his thinking in a simple, structured way. He was fast, practical, and very focused on outcomes.

Our HR process didn’t allow us to hire him directly. We had to get special approval from leadership. We hired him anyway.

Within a few weeks, it was clear we had made the right call. He delivered faster than many experienced hires, not because he “knew more,” but because he could think clearly and adapt quickly. That moment changed how I look at talent.

What AI is changing right now in hiring

Today, that experience feels even more relevant. AI is now doing a lot of the work that used to help candidates stand out, right from basic research, writing first drafts, building summaries, and even structuring analysis.

We are seeing these changes across industries and, more importantly, across our own client ecosystem, where AI adoption is already becoming operational, not experimental.

For instance, BASIC Home Loan (a leading fintech) uses AI to simplify home loan journeys and reduce friction in early-stage processing, while KhiladiPro (sports tech startup), uses AI to make sense of large volumes of sports and engagement data. Both cases reflect something important: our clients are not just using AI; they are becoming AI-native in how they redesign workflows. And that changes what we value in people.

Also Read: What to actually prioritise when your board wants AI and everything feels urgent

This is not just a company-level shift. It is showing up in the broader labour market as well. The India Skills Report 2026 reinforces this transition: India’s employability stands at 56.35 per cent, even as demand rises sharply for digital and AI-related skills. India contributes about 16 per cent of global AI talent, positioning it as a key global supply hub; and by 2027, the country is expected to have 1.25 million AI specialists.

Taken together, the signal is clear: AI is not just changing tools inside companies, it’s changing how work is structured, and how talent is defined.

So what does a future-ready skill stack look like?

The hiring question has shifted. It is no longer: “Who has the best profile?” It is now: “Who can use new tools to think better and work faster?” Across teams, three shifts are becoming very clear—skills matter less than learning speed, thinking has become the real edge, and execution is now human + AI rather than either/or.

If that is how work is changing, the skill stack is also changing—from isolated abilities to a few connected strengths that work together.

• Industry knowledge + AI fluency: Understanding your sector’s economics, policy shifts, and blind spots is still important, but it now works best with strong AI fluency. The real edge is knowing how to use AI to speed up work, improve decisions, and sharpen thinking—not just to generate output.
• Thinking, data and storytelling: AI can process data, but it cannot decide what matters. The ability to find signals in data, simplify complexity, and turn it into clear, decision-ready storytelling is what helps people influence outcomes.
• Collaboration + communication: As more work becomes AI-assisted, human skills matter more in how teams align and move together. The ability to build trust, persuade, and coordinate across people is becoming a key leadership skill.
• Creativity: Automation improves efficiency, but it does not replace originality. In fact, as execution becomes easier, the ability to think differently becomes even more valuable.

Also Read: Think with AI: The new skill for social entrepreneurs

If I were hiring today

I would still make the same decision I made eight years ago. But I would add one more filter. Not “Which college?” Not “Which degree?”

Instead, I’d ask: Can this person learn continuously? Can they adapt as technology changes? Can they use AI to improve how they think and solve problems? Can they combine human judgment with machine capability? Because that is increasingly the real job.

Final word

The biggest talent shift in the AI era is not that degrees matter less. It’s that learning speed, adaptability, curiosity, and AI fluency now matter more than ever before. The strongest teams of the future will not simply be defined by credentials or polished resumes. They will be defined by how quickly they can learn, evolve, and work alongside AI to create meaningful outcomes.

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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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There is a number buried in the Agentic Economy Report by blockchain firm Morph that should concern every payments executive in Asia: US$0.20. That is the average size of an x402 agent payment as of March 2026, according to on-chain analytics firm Lookonchain. It is also below the fixed per-transaction floor of every major card network on the planet.

Card rails were engineered for human-sized purchases. A US$0.20 payment — the kind an AI agent makes when it calls an API, queries a data source, or pays for a single unit of compute — is not economically viable on Visa or Mastercard. It is not that the networks choose to exclude this transaction category. It is their cost structure that makes it impossible to serve.

Also Read: The invisible shopper rewriting Asia’s e-commerce playbook

This is the core insight behind the Morph report’s Prediction 2: most agent-initiated payments, by transaction count, will settle outside traditional card rails.

The x402 protocol and Asia’s stablecoin moment

The mechanism enabling this shift is x402, an HTTP 402-based protocol for stablecoin micropayments at API call time. The standard was co-developed by Coinbase, Cloudflare, and the x402 Foundation, and Google’s AP2 added a native x402 extension for stablecoin settlement when it launched with 60-plus partners in September 2025. The protocol is elegant in its simplicity: when an agent requests a resource, the server responds with a 402 (Payment Required) status, the agent pays in stablecoin, and access is granted — all without a human card number, a payment gateway, or a merchant acquirer taking a slice.

The current volume is modest, but the trajectory is being watched carefully. Bloomberg, citing x402.org data, reported approximately US$24 million in 30-day x402 agent payment volume in early 2026. Andreessen Horowitz’s crypto research team placed the wash-trade-filtered figure at roughly US$1.6 million over the same window, a 15-fold gap that the Morph report acknowledges directly, noting that raw volume is not yet a reliable indicator of commerce. What is reliable, it argues, is the speed of protocol integration at major payments, retail, and platform companies.

For Southeast Asia and broader Asia Pacific, where cross-border payment friction remains high, and stablecoin adoption has grown fastest among the world’s unbanked and underbanked populations, the structural opportunity is substantial. Circle’s USDC Gateway is already built to batch thousands of micropayments before on-chain settlement, a design choice, the report notes, “that is only useful when machines, not people, are the senders.”

The agent overtakes the human

Prediction 3 in the Morph report is the most structurally significant for the stablecoin sector: AI agents will overtake humans in commercial stablecoin payments by transaction count. The claim is deliberately narrow; it is about machines paying for real goods, services, or API capacity, not human transfers or conventional trading bots.

Also Read: Stablecoins are becoming ‘dollars as a service’ for emerging markets

The case rests on two diverging trend lines. Human commercial stablecoin payments — people buying goods with USDC or USDT — are bottlenecked by merchant acceptance, retail onboarding, and regulatory uncertainty. They compound slowly, year over year, at a rate of low-to-mid millions of transactions globally. Agent commercial payments start from near zero and compound monthly. Three drivers make this credible.

MCP SDK installs grew from 5 million monthly in February 2025 to 97 million monthly by March 2026, putting agent tooling in the hands of virtually every enterprise developer worldwide. ERC-8004’s live deployment on the Ethereum mainnet provides agents with a portable identity and on-chain reputation not owned by any single platform, removing a key barrier to agent acceptance at the merchant layer. And the Bank for International Settlements, in Working Paper 1310, concluded that routine cash-management tasks could be automated using general-purpose large language models at institutional quality.

Tether’s CEO, Paolo Ardoino, has publicly set the endpoint at “one trillion AI agents” transacting on-chain within 15 years. The technical ceiling, the Morph report argues, is gone. “The commercial ramp is what is left.”

The institutional layer is the harder problem

For Asia’s banks, payment networks, and fintech infrastructure providers, the more pressing question is not whether stablecoins will carry machine-to-machine volume — that outcome is increasingly structurally inevitable — but whether they will build the institutional rails to handle it under compliance regimes.

JPMorgan’s Kinexys platform now processes US$5 to US$7 billion daily and has launched USD deposit tokens on Base. Project Agorá, co-ordinated by the BIS, is in testing with seven central banks on a tokenised, programmable cross-border rail. Fed Governor Christopher Waller delivered four public speeches on agentic AI in payments between September 2025 and February 2026.

Also Read: The growing adoption of Ethereum in emerging markets

The report quotes Richard Astle, VP Head of Network at Fireblocks, framing the institutional challenge precisely: “Letting an agent transact on behalf of a user, at scale, and under a real compliance regime is a question of how you delegate authority without delegating custody, how you enforce policy at agent speed, and how you reach end users without rebuilding the stack underneath them.”

For Singapore, which has positioned itself as Asia’s digital asset regulatory sandbox, and for the broader region’s central banks navigating their own stablecoin frameworks, this is the operative design question of the next 24 months. The volume is coming. The question is which rails it clears on.

The post The US$0.20 payment that could rewire Asia’s financial rails appeared first on e27.

Every CV in my inbox is a perfect fit. All keywords match. All important tools are listed. The bullet points read like the job description, but slightly rearranged.

Three years ago, this meant something. Now it tells me almost nothing.

The real interview starts when the candidate has to speak without a script. That’s where the gap keeps showing up: young people who can produce output but can’t defend or question it, and often can’t explain how they got there.

We’re not being honest about this part.

The AI-native hiring pitch

Everybody keeps saying: junior hires are AI natives. They are faster, cheaper, and more fluent with the tools. They’ll out-execute mid-level employees still doing things the old way. Hire young, hire AI-fluent, scale your startup with half the headcount.

As a startup founder, I see the appeal. Most of us are looking for places where AI can replace manual processes and where output can grow without payroll growing with it.

The hiring decision that follows from it is where things stop looking so straightforward.

What the data says

Younger candidates are more comfortable with AI on average. That’s true. They reach for it without thinking. They save real time on tedious work.

But being comfortable with a tool is not exactly the same as being good at your work.

A 2025 Microsoft and Carnegie Mellon study surveyed 319 knowledge workers and found that the more someone trusted GenAI to handle a task, the less critical thinking they brought to it. Gerlich’s research on 666 participants showed that people aged 17 to 25 had the highest AI dependence and the lowest critical thinking scores of any age group. In engineering, a recent analysis found that junior developers accept around 89 per cent of AI-generated suggestions without seriously reviewing them.

Also Read: What to actually prioritise when your board wants AI and everything feels urgent

There’s something deeper going on underneath those numbers. In a Harvard Business Review piece from February, consulting partner David Duncan notes that generative AI was helping him much more than it was helping his junior colleagues. People with experience can tell whether AI output is good. People without it can’t.

So the question isn’t whether juniors are bad at using AI. They’re not. The question is what happened to the work that used to make them good at everything else.

Where junior judgment used to come from

The boring tasks that AI now handles, like first drafts, data cleaning, call summaries, and basic reports, weren’t just boring work. They were the apprenticeship. You learned what a good campaign brief looked like by writing fifty bad ones. You figured out which client signals mattered by sitting through the call and writing the notes yourself.

Researchers at IESE have started calling this the “skills pipeline” problem. Without the grunt work, it gets hard to imagine how anyone develops the expertise to step into senior roles. The same automation that boosts productivity this quarter can leave you without strong people to put in strategic positions three years from now.

So when I watch a junior confidently present AI-generated work, the question in my head is: where did your judgment come from? Sometimes there’s a good answer. Often there isn’t.

The other half of the gap

The flip side of the over-trusting junior is the under-adopting senior. In a startup, that one costs you just as much.

Senior people bring context, scepticism, and pattern recognition that catches AI errors. But a lot of them are slow to pick up the tools. Sometimes it’s a habit. Sometimes it’s that they’ve spent years being rewarded for doing things carefully and by hand. BCG’s 2025 AI at Work survey found that while three-quarters of leaders and managers use generative AI weekly, only 51 per cent of frontline employees do. The adoption gap runs across roles, not just generations.

A small company can’t afford either version of this. The junior who trusts AI too much and the senior who uses it too little have exactly what the other is missing.

Also Read: How AI is changing what an SME team actually looks like

Rebuilding the apprenticeship loop on purpose

If AI replaces the work that built judgment, hiring for judgment won’t be enough. You have to engineer it back in. In a startup with no patience for slow on-the-job learning, that means designing for it deliberately. Here are the four things to consider.

If AI ate the work that built judgment, hiring for judgment won’t be enough. You have to engineer it back in. Four things are worth trying.

• Score judgment, not output: Evaluate junior staff based on the questions they ask before execution, not the polish of what comes out. A junior who flags three problems with the delivery is worth more than one who turns in a clean draft on the first try. The World Economic Forum calls this the shift from execution to discernment.
• Pair juniors and seniors (both ways): Seniors review juniors for missed nuance. Juniors show seniors what the tools can already do. IDC and CIO Magazine now treat this as baseline practice, not a perk.
• Put juniors near the consequences early: Client calls from week one, not month six. As CTO Magazine put it, judgment forms through “imperfect decisions, confronting trade-offs, and experiencing feedback loops.” Without exposure, it doesn’t form at all.
• Make AI adoption a KPI for everyone: Expect seniors to use AI weekly, log where it fails them, and share those failures in team reviews. Seniors engage with the tools. Juniors see what informed scepticism actually looks like. Short internal hackathons, real workflow, mixed pairs. This makes work a “forcing function”.

What does this change about hiring?

“AI-fluent junior” as a category is incomplete. Fluency without judgment gives you confident-sounding work that can come out as faulty under review. Judgment without fluency gives you careful work that arrives a week too late.

What a small company needs is not a different kind of hire. We need a different setup for the hire. Stop looking for the candidate who already has both sets of skills – those profiles are very rare. Hire for the strength they have, and build what’s missing on the job.

The companies that work this out won’t be the ones with the most AI-fluent juniors. They’ll be the ones who still know how to grow a senior.

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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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AI tools may one day be better than any human at many things. It’s possible an AI system could become a better writer, artist and coder. The popular narrative is that humans will always dominate in roles where empathy, collaboration, and social interaction are important. We encourage people to spend more time on soft skills, collaboration and communication.

What if this isn’t true? In some settings, humans are reliant on AI tools for therapy and connection. Some humans prefer the company of animals to that of other humans. It seems reasonable that there will be a future where some humans prefer the company of robots to humans. We can already see signs of this through the dark-mirror-esque recent story from Wired about the wives of Silicon Valley founders. And all the reports of people seeking advice from chatbots rather than doctors or mental health professionals.

I can imagine a future where many people prefer a non-human system for many things that today we rely on humans for. We accept non-human decision-making in a wide range of industries and experiences today. Yet, just because AI systems and tools can do one or many things that humans do today better tomorrow, doesn’t automatically mean that humans will have no work to do. That is a false equivalence. There are an almost infinite number of possible futures where humans and automated systems work in different, varied ways.

Humans will remain relevant with or without AI tools because we employ people mainly to work with people, not to do mindless, automatable tasks. The majority of time in a typical job isn’t especially productive. People chat over coffee, handle personal matters, speak with customers, suppliers and partners and sit in endless meetings about things they should or could or might be doing. Productive, deep work does happen for most people, yet it’s often done around the other messy-humans-working-with-humans work. Even if you get a perfect answer every time, who’s going to build it with you? Once you have optimised your time beyond a certain point, there isn’t any more flex. We all need other people to get anything meaningful done.

Also Read: A 65% probability explains the next likely move for Bitcoin as leverage clears

Chatting with LLMs, chatbots, and agent tools can be interesting and fun, but it is also superficial. The longer someone chats with a chatbot, the more endearing the complexity of a human conversation becomes. Only humans can do many things with other humans. Only a human can have deep technical knowledge about a topic, have experienced that domain, and sit down to talk with you about their kids, their hobbies and complex problems. Only humans can create art that has a human story behind it. AI systems will certainly write amazing stories and generate spectacular art. But once it’s prolific, it’s worthless. We value things made by humans because of their scarcity, personality and story.

This is obvious to students and the majority of people who are increasingly negative about AI, yet it seems invisible to many leaders. I suspect this is because of the distance abstraction creates. Leaders of companies abstract themselves from the front-line work. Even a mid-sized company is simply too large to keep track of everything. Sometimes by choice or personality, managers increasingly abstract their connections with work and people. “It’s lonely at the top,” as the saying goes. All the potential possibility of AI systems fits nicely into this simplified abstraction. The messy, unproductive part of work doesn’t fit well in burndown charts, productivity metrics and token consumption. AI tools offer the illusion of a simple, abstracted solution. The popular narrative at the top is some version of “we can carry on as-is, don’t worry, everything will be amazing with AI.”

Like previous similar cycles of technology transformation and corporate profiteering, we will see a lot more damage before things improve. A lot of people will lose their jobs and suffer due to shortsighted, distant management. After a time, those companies will hire people back once they realise that all that human-to-human inefficiency isn’t easily replaced with a probabilistic, generative system. Or when, more carefully, quiet companies step up and fill the gaps. Eventually, the delusional returns will not materialise, and high-risk, high-return investors will pull back and look for something else to gamble on.

Also Read: Emotional intelligence makes AI training stick

The impact on knowledge workers will be terrible and cyclical. And while we are all distracted by this AI illusion, development funding cuts, war, environmental disasters, and global warming are taking away the futures of the children of the world. For the majority of workers, they will continue to do what they have always done, make the best of things in spite of clueless management. All of us will continue to make the best of the tools we are given, as we have done with ERPs, cloud computing and every iteration of the technology transformation eras. We aren’t going through an AI, cognitive boom. We are going through a social, political, and moral crisis. Developed economies have lost their way, established tech companies don’t know how to further juice their returns, and AI is the newest appealing distraction from real problems without easy solutions.

Humans won’t be replaced by robots or AI systems. It’s frustrating that we even need to debate this point. The debate alone shows us how far the social narrative has abstracted away from the reality of how our world works. The world is full of jagged edges that don’t fit in rapid-prototype-iteration cycles. AI tools are just another technology transformation trend. Like previous tech tools, they are amazing and offer lots of exciting new possibilities. And they are distracting us from the things that are harming humanity.

We cannot drink tokens, eat GPUs, live in data centres, or resolve wars with chatbots. The majority of the world is happening outside of the AI-obsessed global north. We used to chide each other about “the world happening outside our window,” yet now we are told to obsess over the world running on our phones and in our heads. Humans tire easily, and I expect we will soon tire of the obsession with superficial AI-enabled commerce.

Even a perfect AI system will be boring at a coffee table. We love to complain about each other and will go to great lengths to avoid interactions with each other. Yet we also love each other and find ways to care and support each other in so many ways. There is so much beauty and love in humanity. We don’t need AI to replace us if we just stop and look for a moment at what we have created, both the beauty and the horror.

We can imagine a better future for all of us with all kinds of technologies if we first invest in each other and the world outside of ourselves.

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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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Every week, a new capability update. The labs are in an uncontrollable race. And whoever achieves AGI will dominate the world, or so the headline goes.

The reality is more nuanced.

Every major technology in history has triggered a moral panic. Bicycles. Television. The car. People fear rapid change, and AI is moving at a speed that makes all of those look slow. But there’s a critical difference this time: we’re not replacing how we move. We’re replacing how we think.

We are in the middle of a cognitive revolution. And the question isn’t whether AI will replace you. The question is whether you’ll let it think for you, or learn to use it to think like yourself, but sharper.

I’ve been building with AI since before it was cool

I’ve been a programmer for 25 years. I’ve been interested in AI since university. But in 2023, something shifted.

I was building a full AI application, calling the ChatGPT API directly, and I remember the exact moment it hit me, the level of reasoning I was getting back from a machine was something I hadn’t thought possible even twelve months earlier. I started using AI every single day. It became a thinking partner, a coding partner, a sounding board.

And then, quietly, something started to slip.

Two weeks in a yurt: No electricity, no escape

Last year, I spent two weeks in a yurt in Mongolia. No electricity. A small solar panel to barely keep my phone alive. No notifications. No doomscrolling. Just my own thoughts and an uncomfortable amount of silence.

It was disorienting in a way I didn’t expect.

After a few days, I realised I wasn’t sure where my own thinking ended, and the algorithmic output began. I had an inner voice that wanted to surface, insights, instincts, observations, but I was feeling confused at the same time. Had I actually thought this? Or had I absorbed it from a hundred ChatGPT sessions and a thousand social media posts or podcasts?

That discomfort became a signal worth following.

Also Read: A 65% probability explains the next likely move for Bitcoin as leverage clears

Attention is the asset nobody is protecting

When I got back, I made one radical change: I carved out 45 minutes every day with no phone, no AI, no social media. Just my own thoughts and a place to write them down.

What I found surprised me. There was an enormous amount of unprocessed thinking inside me that had never been given space. It was being hijacked, first by social media, then by AI itself.

Microsoft’s 2023 research flagged that heavy AI reliance risks atrophying certain cognitive skills. The World Economic Forum’s 2025 Future of Jobs report identifies critical thinking and creativity as the most irreplaceable human skills going forward. And yet the average person now spends over seven hours a day on screens, much of it in passive consumption.

We are not strengthening our thinking. We are outsourcing it, and we’re doing it without noticing.

Attention is capital. The question is who you’re deploying it for.

AI as a mirror, not a vending machine

Most people use AI like a vending machine. You put in a prompt, you expect a polished answer. The problem is you get the AI’s internet training data average, not your own.

What I discovered, through deliberate experimentation, is a different configuration entirely: using AI as a cognitive mirror. Not asking it to generate answers, but to reflect back the thinking I had already produced independently.

The sequence matters enormously.

First, I write. Privately, without AI. I capture raw thought, unfiltered, unpolished, mine. Then I bring AI in to reflect patterns, surface blind spots, and push me to go deeper. The AI becomes an accelerant for my own generative engine, not a replacement for it.

The output is categorically different. When AI reflects your own thinking back to you, it stops feeling like a search engine and starts functioning like a rigorous thinking partner who actually knows your material.

Your own data is the most valuable asset you’re not building

Here’s the practical implication most people are missing.

AI is getting better at generating answers. But humans are getting worse at generating original questions, because we’ve stopped spending time alone with our own minds.

The discipline I built post-Mongolia is simple: produce your own data, independent of AI. Write down your observations and commit them to GitHub. Over time, it becomes a private repository for your mind, a record of how you think, what you notice, and what genuinely matters to you.

In a world increasingly shaped by generated content, preserving an uncompressed trail of your own thinking becomes an advantage.

This isn’t nostalgia for analogue life. It’s a strategic decision. The people who will navigate the AI era well are those who have a strong, legible relationship with their own thinking, and can use AI to amplify it rather than replace it.

Also Read: Emotional intelligence makes AI training stick

We are coming from a social media hangover

We are not ready for AI. Not because the technology is too advanced, but because we arrived here already depleted.

Our attention has been systematically harvested for over 14 years. We are arriving at the most cognitively demanding technological transition in human history in a weakened state, distracted, reactive, and heavily dependent on external validation loops.

AI is not the problem. But AI layered on top of an already-fragmented attention span is a compounding risk most people are ignoring.

The manifesto I had to write

Everything I’ve described here, the Mongolia reset, the attention discipline, the AI mirror framework, the practice of generating your own cognitive data, became the foundation of a book I had to write.

I called it Attention OS: a practical manifesto for anyone who wants to defend their independent thinking, upgrade how they use AI, and build a sustainable human-AI interface that actually works for them.

The core provocation is simple: what if you stopped asking AI for answers, and used it to map how you already think?

It’s not anti-AI. I use AI every day and consider it one of the most powerful tools I’ve ever had access to. But a tool is only as good as the person wielding it. And right now, most of us are being wielded by the tool.

The question worth sitting with

AI is already inside your head, whether you’ve noticed or not.

The talent reset everyone is talking about isn’t just about which skills survive automation. It’s about whether you’ve maintained a strong enough connection to your own thinking to remain the author of your own decisions.

That’s not a technology question. That’s a human question.

And the time to answer it is before the default answer gets chosen for you.

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Every communicator obsesses over their audience. Demographics. Psychographics. Scroll behaviour. But in 2026, there’s a new audience in the room, one that never sleeps, never skips, and decides whether your brand is worth knowing before any human even asks.

That audience is AI. And most brands are still performing for the wrong crowd.

When did you last actually click a search result? Not skim the snippet. Not read the AI summary. Click through. If you’re honest, less and less. Because the answer is already there. Synthesised, summarised, served. No click required.

We’ve entered the zero-click era. And for every brand that built its strategy around being found, this isn’t a trend to monitor. It’s a structural collapse of the old playbook.

The shift nobody’s talking about loudly enough

For years, communications ran on one metric: visibility. Impressions. Rankings. Share of voice. We chased the algorithm as it owed us something.

Then it got smarter than us.

Today, ChatGPT, Perplexity, and Google AI Overviews send a fraction of the traffic to your websites compared to what search engines used to. They have become the answer. They synthesise, summarise, and cite. The brands that appear in those answers aren’t the loudest or the best-funded. They’re the ones AI has decided to trust.

That’s the real shift. Not visibility versus irrelevance. Authority versus noise.

And the distance between those two things is the distance between building a brand and burning a budget.

Welcome to the citation economy

In the old world, influence was measured in clicks. In the new world, it’s measured in citations.

When an AI system answers a question, and your brand’s perspective shapes that answer, without a paid placement, without a sponsored tag, without a single cookie, you haven’t won a news cycle. You’ve been written into the record.

When AI cites you without being prompted, you’ve transcended marketing. You’ve become knowledge itself.

That’s not hyperbole. That’s the new competitive frontier.

Also Read: A 65% probability explains the next likely move for Bitcoin as leverage clears

The data makes it stark: more than 85 per cent of AI citations reference earned media, third-party journalism, credible reporting, expert commentary, not brand-owned content, according to a 2026 report from Muck Rack. The brands winning in the citation economy aren’t the ones shouting loudest on their own channels. They’re the ones being vouched for by sources AI already trusts.

This isn’t a PR story. It’s a brand-building story. A growth story. For every CMO asked to prove ROI, every founder trying to build authority in a crowded market, this is your answer hiding in plain sight.

Earned media is the algorithm now

Here’s the irony that should stop every performance marketer cold: the discipline historically hardest to measure is the one AI rewards most.

Because AI systems aren’t running a popularity contest. They’re running a credibility audit. Every time a user asks a question, the system asks: Who validates this brand? Are those sources authoritative? Is the narrative consistent across independent voices?

Structured credibility. Third-party validation. Authoritative presence in trusted publications. These were never soft PR goals. In 2026, they are the technical infrastructure of brand discoverability.

Success isn’t being found at the top of a page anymore. It’s being embedded in the answer itself.

Three moves, no shortcuts

Winning the citation economy takes discipline, not volume. The earned-first approach our industry has practised for decades is now the technical layer AI runs on.

• Be a knowledge source, not a content factory: The brands getting cited aren’t publishing more. They’re publishing better, sharper, more specific, more authoritative. They take positions. They produce original data. AI rewards substance and punishes the generic. Frequency is not a strategy. Credibility is.
• Relinquish control to earn influence: This is the hardest shift for brand teams wired for message control. Your narrative is now co-authored by journalists, analysts, and credible third parties, not just your content team. What others say about your brand story matters more than the story you tell about yourself. Trust earned externally is the only trust AI amplifies.
• Measure what actually matters now: The new questions aren’t “Did they visit?” They’re “Did our brand appear in the AI answer? Was it accurate? Was it credible?” Citation frequency, AI share of voice, and quality of context are the metrics that will define communications effectiveness for the next decade.

The ones who win

The brands that thrive won’t be the ones that treat AI as a content shortcut. They’ll be the ones that treat AI as an audience, discerning, credibility-driven, impossible to game. An audience that reads everything, trusts selectively, and cites only what it believes is worth knowing.

Also Read: Emotional intelligence makes AI training stick

But citation cuts both ways. AI systems hallucinate. They compress. They inherit the biases of whatever sources shaped them, and when they get your brand wrong, they do it at scale, authoritatively, and without a correction notice. A misattributed position, a conflated acquisition, an outdated narrative baked into a model’s weights, these don’t disappear with a counter-press release. The new reputation crisis isn’t a bad headline. It’s a bad citation that ten million queries will repeat without question.

This is where communications earns its seat at the strategic table. Not just in shaping what AI learns about your brand, but in monitoring, challenging, and correcting what it believes. Narrative correction in the AI era isn’t reactive crisis management. It’s a proactive, always-on discipline. And right now, very few organisations are built for it.

PR should be. And in the Asia Pacific, the urgency is compounded. Baidu’s citation logic, Naver’s AI summaries, WeChat’s closed-ecosystem intelligence, and the regional LLMs reshaping search behaviour across Southeast Asia do not behave like the Western AI stack. There is no single algorithm to optimise for. There is only the harder, more durable work of building credibility that travels across languages, platforms, and AI architectures simultaneously. In a region this narratively rich and commercially dynamic, that’s not a constraint. It’s an advantage for those willing to do the work.

The future belongs to those fluent in data and eloquent in humanity. Machine-readable enough to be trusted. Human enough to be believed.

The question is no longer: are you being found?

It’s: Are you being cited, and do you control what that citation says?

If you’re not certain, you’re already behind.

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The financial markets currently present a fascinating divergence between traditional equities and digital assets. Investors actively rotate capital out of high-technology names and into defensive sectors. The crypto market experiences a severe deleveraging event at the exact same time. We witness the traditional gambling halls of Wall Street pivot toward safety while the crypto casino clears out overleveraged participants. This dynamic offers a perfect lens to examine the convergence of artificial intelligence, decentralised finance, and macroeconomic policy.

Bitcoin slid from the mid-US$70,000 range down to intraday lows around US$61,300 between June 2 and June 4. This drop marks the weakest level since early February and completely wiped out approximately US$1.6 billion in leverage. Derivatives trackers confirm that exchanges liquidated roughly US$1.2 billion to US$1.8 billion in leveraged positions over 24-hour periods. Long positions took the vast majority of this hit while open interest reset to lower levels.

Altcoins tracked this downward trajectory perfectly. Solana and Cardano dropped to multi-year lows while XRP logged steep drawdowns and new year-to-date lows. Furthermore, US spot Bitcoin ETFs endured 13 straight sessions of net outflows, draining about US$4.3 billion to US$4.4 billion since May 15. BlackRock IBIT drove much of this selling pressure. MicroStrategy also disclosed a sale of 32 BTC on June 1, marking its first sale since 2022. While small relative to total holdings, the market interprets this as a sentiment signal. I see this purely as a necessary cleansing of speculative excess. The market must clear out weak hands before any sustainable upward movement can occur. We now watch whether Bitcoin holds support in the low US$60,000 range and whether ETF flows stabilise.

Traditional equity markets tell a completely different story of rotation rather than outright retreat. The Dow Jones Industrial Average surged 1.7 per cent to a record close as investors actively pulled money out of artificial intelligence stocks. The S&P 500 rose 0.4 per cent while the Nasdaq closed completely flat. This stagnation in the technology-focused index stems directly from Broadcom plunging 12 per cent. The market executed a notable pivot from technology, semiconductors, and memory stocks into defensive pockets like healthcare, financials, telecommunications, and real estate.

UnitedHealth, JPMorgan, Costco, and Eli Lilly led these gains outside the technology sector. The Russell 2000 also performed exceptionally well, closing exactly one point off a fresh record high and outperforming the broader indices of large companies by a wide margin. This behaviour perfectly illustrates my long-held view that public markets will regain popularity among entrepreneurs and provide broader access to investment opportunities, but only when valuations reset to rational levels. Investors simply refuse to pay premium multiples for tech stocks right now.

Corporate earnings data reinforces this rotation away from pure artificial intelligence hype. Broadcom reported second-quarter fiscal 2026 revenue up 48 per cent to US$22.19 billion. This figure narrowly missed consensus expectations. Their artificial intelligence revenue surged 143 per cent to US$10.8 billion. The company reiterated rather than raised its fiscal 2027 artificial intelligence target above US$100 billion, prompting the massive 12.5 per cent drop in shares.

CrowdStrike delivered first-quarter fiscal 2027 revenue up 26 per cent to US$1.39 billion, beating consensus by around 2 per cent. Earnings per share hit US$1.10, beating estimates, and shares still fell 3.8 per cent on soft guidance. The market demands absolute perfection from these technology names and punishes any hint of deceleration. This creates an environment where speculative financial activities like stock trading feel exactly like gambling, just with slightly better odds than traditional casinos.

We also see a monumental shift in how capital markets value the convergence of physical and digital infrastructure. SpaceX set terms for a record US$75 billion initial public offering at a staggering US$1.75 trillion valuation. The company will sell 555.6 million shares at US$135 each, making this the largest IPO in history. Trading begins June 12 under the ticker SPCX. Lead investment bank Goldman Sachs expects the company’s artificial intelligence revenues to surge 100x by 2030 to US$322 billion. This projection aligns perfectly with my research on Web4, where artificial intelligence and physical network infrastructure merge to create entirely new economic layers.

The market recognises that the next generation of value creation will not come from pure software but from the integration of intelligent systems with global connectivity. This specific intersection defines the core thesis of my upcoming book on Web4. We are moving past simple digital ledgers into an era where autonomous agents manage decentralised networks. The sheer scale of this SpaceX offering proves that institutional capital finally understands this major technological shift.

Geopolitical developments also played a crucial role in shaping market sentiment during this period. Brent crude oil fell 2.0 per cent to US$95.35 after Israel and Lebanon agreed to a ceasefire. This agreement lifted hopes for a broader deal between the United States and Iran and a potential reopening of the Strait of Hormuz.

Although negotiations between the United States and Iran remain in a deadlock, statements indicating a desire to avoid restarting attacks provided some relief to energy markets. This reduction in geopolitical risk premium directly supports the rotation into defensive equities and removes a major headwind for global economic growth.

Macroeconomic indicators present a mixed picture, further complicating the investment landscape. US initial jobless claims rose to 225,000, missing the forecast of 214,000 and increasing from 212,000 the prior week. Markets now await May nonfarm payrolls, with consensus expecting around 85,000 jobs, down from 115,000 in April. A third straight month of gains would signal a resilient labour market despite higher interest rates.

Meanwhile, Eurozone retail sales fell 0.4 per cent month on month in April, performing worse than the expected 0.3 per cent decline following a 0.8 per cent rise in March. These diverging economic signals force investors to make difficult choices. They must balance the resilience of the American worker against the fragility of the European consumer. This exact tension drives the current market volatility and dictates the flow of global capital.

Ultimately, we observe a massive reallocation of capital across all asset classes. The crypto market must complete its deleveraging phase, and a slowdown in forced liquidations, combined with improving US spot Bitcoin ETF demand, will signal the exhaustion of this downtrend.

Simultaneously, traditional markets are pricing in a new reality in which artificial intelligence companies must deliver flawless execution to justify their valuations. The SpaceX IPO represents the ultimate test of this new standard, bridging the gap between physical space infrastructure and digital intelligence.

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Asia’s merchants are approaching a strategic inflection point: in agentic commerce, the contest is no longer won at the search-result page, but inside the machine’s decision loop.

Morph’s forecast may sound bold, yet the surrounding evidence from Salesforce, Adobe, and McKinsey suggests the foundations are already in place. Open protocols for identity, mandate, checkout and settlement mean AI agents can increasingly transact like autonomous buyers rather than glorified recommendation engines.

For Asian retailers, this is both liberating and brutal. It lowers historic frictions in cross-border trade, giving merchants in Jakarta, Bengaluru, or Ho Chi Minh City access to buyers with fewer checkout penalties. But it also weakens familiar moats. If agents optimise relentlessly for price, delivery, reviews and return terms, then brand premium, ad spend and marketplace ranking lose defensive power. Merchants built around search arbitrage and performance marketing may discover that the most valuable shelf space is no longer Google or Meta, but the agent’s preferred merchant graph.

The starkest line in the report is also the clearest strategic warning: carts will not be abandoned; merchants will be. In the agentic era, readiness becomes survival. Those who expose inventory, pricing and payments to agents early will capture the demand wave.

Regional

Grab pushes back on Indonesia exit reports amid GoTo merger talk: The super app firmly denied rumours of a market exit, reaffirming its decade-long presence and ~50% ride-hailing market share in Indonesia, as speculation swirls over a potential merger with local rival GoTo.

SG Enviro closes Series A to serve data centres, fabs in SEA: The Singapore-based water and wastewater treatment firm, backed by Emerald Ventures and SEEDS Capital, now targets the region’s fast-growing semiconductor, pharmaceutical, and data centre industries with modular solutions.

VinRobotics takes its humanoid robot to ICRA and COMPUTEX: Vingroup’s robotics arm debuted the VR-H3 at two of the world’s premier tech and robotics stages, positioning Vietnam as a serious contender in the global humanoid hardware race.

Cool Japan backs JumpStart’s Series C as AI vending machines scale: Japanese brand distributor Cool Japan Fund joins JumpStart’s Series C as the Indonesian startup’s AI-powered vending network surpasses 6,500 machines with 200% revenue growth, reshaping last-mile retail.

Chinese autonomous firm Neolix partners QuikBot in Singapore: The tie-up brings door-to-door autonomous delivery to Singapore, with Punggol Digital District serving as the initial testbed for Neolix’s self-driving vehicles operating under QuikBot’s logistics platform.

Indonesian crypto platform Floq raises US$11.3M in under a year: The platform, which amassed 1.8M users since launching, secured fresh capital to deepen its crypto offerings amid Indonesia’s growing retail digital asset adoption.

Singapore’s K25.ai secures US$6M commitment from NewGen: The AI startup closed the funding at an implied valuation of ~US$100M, with NewGenIVF backing its ambition to deploy AI-driven solutions across Southeast Asian markets.

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Interviews & Features

Data, not hardware, is the real bottleneck in humanoids: Matrix Robotics CEO Allen Zhang says the scarcity of training data, not component costs, is slowing the industry. He targets 10,000 units by 2028 and predicts a shakeout that will leave only a few survivors.

‘We are confident enough’: Nicko Widjaja’s lawyers speak out: BRI Ventures CEO Nicko Widjaja faces an 11-year prosecution over a TaniHub investment decision. His legal defence team invokes the business judgment rule, arguing the case criminalises standard venture capital practice.

From a small town in Spain, Magnific quietly reshapes creative AI: CEO Joaquín Cuenca Abela built Magnific into a globally ranked creative platform, ranked 11th by a16z, while staying bootstrapped. India and Indonesia now form its largest user base outside the West.

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International

Agentic commerce is quietly rewriting Asia’s e-commerce playbook: AI agents are emerging as autonomous buyers across Asia, with McKinsey, Salesforce, and Adobe data pointing to a US$3-5T opportunity that will fundamentally alter search, advertising, and brand discovery by 2028.

DeepSeek eyes US$7.4B in its first-ever fundraising round: The Chinese AI lab, valued between US$51.8B and US$59.2B, is drawing interest from Tencent and CATL as it seeks capital to scale its globally disruptive large language model business.

Uber commits ~US$500M to self-driving startup Nuro: The robotaxi partnership marks one of Uber’s largest autonomous vehicle bets, with Nuro set to expand driverless testing and move toward commercial passenger service under the Uber platform.

Crypto market faces liquidation spiral risk toward US$2T: Over US$370M in Bitcoin liquidations have already hit the market, with geopolitical triggers and leveraged positioning raising the risk of an extended downturn toward a US$2.17T total market cap support level.

A 65% probability explains Bitcoin’s next likely move: With US$789M in liquidations recorded and 11 consecutive days of ETF outflows, on-chain data and MicroStrategy’s first Bitcoin sale since 2022 point to a probable near-term directional move for the leading cryptocurrency.

SoftBank shares drop 10.6% amid broader tech sell-off: Shares fell sharply on renewed concerns about the conglomerate’s heavy AI investment exposure, despite the stock having gained ~70% year-to-date on bullish bets tied to its portfolio companies.

Korea Investment & Securities acquires 20% stake in Coinone: The move signals a major brokerage’s formal entry into digital assets, as South Korea’s financial incumbents accelerate integration with the country’s well-established crypto exchange ecosystem.

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Cybersecurity

Japan’s top banks get access to Anthropic’s Claude Mythos: MUFG, Mizuho, and SMBC will deploy the model for cyber defence as part of a public-private working group, marking a significant step in Japan’s effort to embed AI into its national financial security infrastructure.

Singapore leads on security governance but struggles to enforce it: A JFrog report reveals that while Singapore ranks highly on security policy adoption, 54% of teams need over a week to produce compliance documentation and 18% lack any formal shadow AI policy.

TrendAI joins Anthropic’s Project Glasswing cybersecurity effort: The Singapore-based firm will use Claude Mythos Preview for AI-powered vulnerability detection, joining a coalition of 150+ organisations across 15+ countries working to raise global cybersecurity resilience.

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Semiconductor

TSMC boss upbeat as AI boom shows no sign of easing: CEO C.C. Wei told investors that AI-driven chip demand remains exceptionally strong, prompting a raised revenue forecast and increased capital expenditure commitments for the world’s largest contract chipmaker.

LG Innotek to expand semiconductor substrate plant in Vietnam: The South Korean firm will begin construction of an expanded facility in Hai Phong in July, with completion targeted for May 2027, deepening the country’s role in the global semiconductor supply chain.

Cerebras says it works with every AI gear maker except Nvidia: The chip startup, whose CS-3 systems run 15x faster than GPU equivalents, counts AWS and OpenAI among its partners — positioning itself as the go-to alternative for AI compute buyers locked out of Nvidia supply.

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AI

ChatGPT hits 1B monthly active users in record time: OpenAI’s flagship app reached the milestone faster than any consumer application in history, even as rival Anthropic’s Claude platform posts 640% year-on-year growth, underscoring a broadening of the generative AI market.

More US firms turn to China’s DeepSeek over pricey AI rivals: Ramp’s trending AI software list shows DeepSeek rising fast despite starting at just 0.3% corporate adoption in January 2025, as cost-conscious enterprises look beyond Anthropic and OpenAI, which still lead at 34.4% and 32.3% respectively.

Nvidia CEO Jensen Huang maps agentic AI’s new computing architecture: Huang outlined a distributed model where RTX Spark handles local inference and cloud handles scale, describing agentic and physical AI as the next frontier for Nvidia’s chip and software ecosystem.

Magnific bets on human-led AI infra for marketing and film work: The creative AI firm launched MCP, Flows, and Agents, enterprise tools designed to maintain brand consistency across campaigns, with a growing footprint across Southeast Asian markets in India and Indonesia.

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Thought Leadership

The invisible shopper rewriting Asia’s e-commerce playbook: As AI agents begin making autonomous purchasing decisions, the US$500B GMV opportunity by 2028 will demand that brands rethink content, SEO, and advertising strategies built for human, not algorithmic, buyers.

The two human skills that make AI-native businesses work: In workplaces where AI handles execution, strategic thinking and systems design emerge as the irreplaceable human competencies — the ability to set direction and architect how work flows, not just complete tasks.

AI didn’t replace my team; it promoted them: Drawing on the contrast between Klarna and IKEA’s AI strategies, the author argues that teams augmented by AI don’t shrink; they move up the value chain, taking on more complex, judgment-heavy responsibilities.

How a 60-year-old cleaning supervisor built an AI agent in 6 weeks: The case challenges the assumption that AI is only for technical workers, showing that deep domain knowledge, not coding ability, is the true competitive moat for building useful, context-aware AI tools in any industry.

The AI trust gap: why SEA startups need proof before they scale: Southeast Asian enterprises remain cautious about AI adoption, demanding a credible “proof stack” of real outcomes before committing to scale, creating both a barrier and a differentiation opportunity for startups that can demonstrate tangible results.

I don’t know what I’m becoming as a marketer, and that’s the point: The author reframes uncertainty as a signal, arguing that marketers who shed the copywriter identity and become conductors of AI-powered workflows are best positioned to lead, especially in SEA’s leapfrog markets.

The always-on boardroom: when strategy stops being an event: AI enables continuous strategic recalibration, dismantling the traditional annual planning cycle. Boards that fail to adapt risk making decisions based on outdated assumptions in an environment where conditions shift in real time.

Great talent used to mean getting things done; now it means knowing what: The shift from executor to catalyst defines the new talent premium. As AI absorbs routine output, the most valuable employees are those who can identify what work matters, and why it does.

The talent question every founder needs to ask before they scale: Scaling prematurely with the wrong people is a compounding liability. The author argues founders must design for capability, not headcount, asking whether their team can adapt to the demands of the next stage, not just the current one.

When AI removes the work that taught us how to think: Delegating cognitive grunt work to AI risks hollowing out the developmental experiences that build judgment. The author warns that removing struggle from learning may produce workers who are fast but fundamentally shallow.

The talent reset: why AI is changing what makes people valuable: As AI commoditises knowledge retrieval, the new currency becomes judgment, discernment, and the ability to operate in ambiguity, shifting what organisations should look for and develop in their people.

AI is quietly redefining how much one person is expected to do: The productivity expectations placed on individuals are expanding as AI absorbs task volume, raising urgent questions about workload sustainability, burnout, and whether “doing more with less” is a strategy or a trap.

The credential trap: what I’ve stopped looking for in risk hires: Degrees and certifications no longer predict how someone handles uncertainty. The author makes the case for composite judgment, assessing how candidates reason under pressure, not how many credentials they can present.

AI didn’t just change the work; it changed who you should be hiring: Adaptability and learning agility now outrank domain expertise on hiring scorecards. The author argues that the best AI-era employees are those who can continuously reinvent their role as the tools beneath them evolve.

Why one person + AI is becoming a serious workforce model: AI crews, autonomous multi-agent pipelines managed by one individual, are shifting the workforce calculus. The author argues execution is now cheap; the scarce resource is the judgment to direct it well.

She spent 3 years as an old woman; your team spent 3 hours: Designer Patricia Moore’s radical empathy experiment becomes a blueprint for inclusive design in Asia’s ageing markets, where the silver economy represents a massive, systematically underserved user base that most product teams still fail to design for.

Greatness in the age of AI: why character outlasts competence: As technical skills get commoditised by AI, the author argues that integrity, resilience, and moral courage are becoming the true differentiators, qualities that cannot be automated, delegated, or replicated by any model.

Emotional intelligence makes AI training stick: Resistance to AI tools often masks identity threat, not skill gaps. The author introduces a six-step framework for helping teams emotionally process the shift, turning psychological safety into the foundation for effective and lasting AI adoption.

When everyone’s talking about OpenClaw and you’re not using it: The author cautions against reflexive adoption of trending AI tools, arguing that intentionality, knowing why a tool fits your workflow, matters more than keeping pace with the hype cycle in a market saturated with new releases.

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Wavemaker Ventures, the early-stage fund of Singapore-headquartered Wavemaker Partners, has led a US$4 million investment round in DataMasque, a New Zealand-founded startup building data de-identification and synthetic data infrastructure for enterprise AI.

Existing investors OIF Ventures and Icehouse Ventures participated in the round, both increasing their positions.

The funding underscores growing investor appetite for tools that help large organisations navigate the tension between AI adoption and data privacy obligations. DataMasque’s platform allows enterprises to use sensitive information — including customer records and call transcripts — for AI model development without that data leaving their own infrastructure.

Paul Santos, co-founder and managing partner of Wavemaker Partners, who joins the DataMasque board as part of the deal, pointed to the company’s demonstrated commercial traction as a key driver of the investment. The firm has helped customers reduce data masking workflows from days to hours and has secured enterprise contracts across financial services, government, and telecommunications sectors globally.

According to Santos, this is a sevenfold speed improvement. “As enterprises feed more data into AI systems, they need a way to protect sensitive information without compromising datasets used for testing and analytics,” he said.

Also Read: The real AI threat isn’t your job, it’s your mind

DataMasque’s customer roster includes New York Life, ADP, Best Western Hotels and Resorts, and One NZ.

Since closing its seed round in late 2023, the Auckland-based company has tripled its headcount and grown annual recurring revenue sixfold. Alongside the fundraiser, DataMasque has launched a capability it describes as a first of its kind: a tool that unlocks unstructured data such as emails, logs, and call transcripts for use in AI and analytics workflows. The company says the vast majority of enterprise data sits in unstructured form and has historically been too privacy-sensitive to activate.

The investment also reflects the strategic importance of Singapore as a target market. DataMasque cited a maturing regulatory environment there, including the 2024 Model AI Governance Framework for Generative AI developed by the Infocomm Media Development Authority, and early 2026 guidance on agentic AI systems, as creating clear compliance requirements that its platform is designed to meet.

Peter Lilley, co-founder of Instaclustr — acquired by NetApp — has also joined the board.

Grant de Leeuw, co-founder and chief executive of DataMasque, said the funding validates the need for high-fidelity synthetic data to support AI model training and evaluation at enterprise scale.

The proceeds will be used to accelerate growth across key markets, positioning the company as a foundational data infrastructure layer for enterprise AI adoption.

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Image Credit: Data Masque

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