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.

—

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 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.

—

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

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

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The 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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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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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.

The post Ecosystem Roundup: The rise of the machine consumer appeared first on e27.

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

The post Wavemaker Ventures leads US$4M round in New Zealand data privacy startup DataMasque appeared first on e27.

A massive liquidation cascade served as the primary driver of this sell-off, which effectively erased over US$370 million in leveraged Bitcoin positions. The sheer scale of these forced liquidations created a self-reinforcing downward spiral that amplified what might have otherwise been a routine risk-off market movement.

The average funding rate consequently flipped negative to -0.00051929, a clear sign that traders are actively paying premiums to sustain short positions. High system leverage effectively acted as fuel for the fire, converting a moderate market pullback into a brutal plunge as over-leveraged long positions rapidly unwound.

This localised crypto turmoil is unfolding against a much broader backdrop of international market instability and escalating geopolitical friction. A strong 93 per cent correlation with the S&P 500 confirms that digital assets are heavily tied to broader macroeconomic shifts rather than trading in isolation. Wall Street recently saw its own historic momentum grind to a sudden halt when the S&P 500 posted its first losing session in nine days.

The index had surged nearly 20 per cent over the preceding nine weeks, an exceptional run that market commentators noted was strong enough to make even the most optimistic investors blush. This defensive pivot across global markets stems from rising oil prices and climbing Treasury yields, both of which are reacting directly to a severe military escalation between the United States and Iran.

The sudden return of energy-shock fears has promptly revived stubborn inflation worries, forcing bond markets to price in a 77 per cent probability that the Federal Reserve will hold its benchmark interest rate unchanged in December, within a range of 3.5 per cent to 3.75 per cent.

Also Read: The tech record vs crypto crash: Why the liquidity roadmap just split in two

The geopolitical situation in the Persian Gulf deteriorated rapidly following a series of highly volatile military exchanges. Iran launched targeted missile and drone attacks directed at Kuwait and Bahrain, with one drone directly striking the passenger terminal at Kuwait International Airport and causing one confirmed fatality. In immediate retaliation, the United States military conducted airstrikes against an Iranian military ground control station situated on Qeshm Island in the strategic Strait of Hormuz.

The confrontation escalated further when American forces deployed a Hellfire missile to target and disable the engine room of an oil tanker that was actively bound for Iran’s Kharg Island. Donald Trump publicly suggested that a diplomatic resolution could still come together fairly quickly, although he simultaneously acknowledged that the current maritime blockade of Iran could easily drag past Labor Day.

This intensifying friction has prompted prominent financial figures to evaluate broader systemic risks, with Goldman Sachs Chief Executive Officer David Solomon noting that markets are currently exhibiting far more greed than fear, supported by ample liquidity that continues to feed massive capital raises like the upcoming SpaceX initial public offering and the recent Alphabet capital raise.

Despite this abundant liquidity, traditional financial markets are showing clear signs of exhaustion alongside digital assets. Commodities closed sharply lower across the board, led by a 5.3 per cent drop in palladium, a 3.1 per cent decline in silver, a 2.9 per cent fall in copper, and a 1.1 per cent slide in gold. Bitcoin faced prolonged selling pressure, dropping an additional 1.7 per cent to hover around US$65,500, bringing its total losses over a three-session span to 11 per cent.

Meanwhile, structural milestones continue to reshape traditional finance, as the Vanguard ETF tracking the S&P 500 officially became the first fund of its kind to amass US$1 trillion in total assets. This massive accumulation of traditional capital contrasts sharply with the recent defensive posture of digital asset investors, who are grappling with structural and institutional headwinds.

Beyond the immediate liquidation crisis, digital assets face significant structural hurdles posed by both regulatory developments and shifting corporate landscapes. Reports indicating that major payment networks like Stripe, Visa, and Mastercard are actively developing their own native stablecoin platforms have triggered widespread anxiety regarding intense competition for industry incumbents.

Also Read: Crypto and equities slide as geopolitical and macro pressures mount

This looming corporate threat severely affected sentiment, causing Circle’s tokenised stock to plunge by more than 10 per cent on June 3. This corporate pressure coincides with a notable cooling of Wall Street enthusiasm for digital assets, evidenced by 12 consecutive days of net outflows from United States spot Bitcoin exchange-traded funds. Institutional caution is growing as market participants realise that impending regulatory shifts and mainstream corporate entries will inevitably create clear winners and losers, threatening established crypto business models.

The near-term trajectory for digital assets remains highly dependent on critical upcoming economic indicators and key technical thresholds. Global markets are focusing intensely on the impending release of United States employment data scheduled for June 6, as a surprisingly robust jobs report would likely validate a hawkish Federal Reserve stance and place additional pressure on speculative risk assets.

From a purely technical perspective, the total cryptocurrency market capitalisation is currently undergoing a vital test of its core support structure at the yearly low of US$2.17 trillion. Maintaining a position above this critical threshold could lay the groundwork for a temporary stabilisation or a short-term relief rally. A decisive daily close below US$2.17 trillion on accelerating volume would effectively validate the current bearish momentum, potentially exposing the market to an extended decline toward the psychologically important US$2.0 trillion zone.

Given the potent mix of forced spot selling, institutional retreat, and geopolitical escalation, the path of least resistance appears tilted toward continued downside risk until macroeconomic conditions stabilise.

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For most of retail’s history, the customer at the end of the transaction was human. They clicked, they compared, they second-guessed.

What is now emerging from the convergence of artificial intelligence, stablecoins, and open payment protocols is something categorically different: a buyer that is software, moves at machine speed, and settles money autonomously.

The Agentic Economy Report by blockchain firm Morph calls this shift “agentic commerce“, and the numbers it cites suggest the shift is not coming. It is already here.

Also Read: When the buyer is a machine: Why agentic commerce threatens the trillion-dollar advertising model

During a single Cyber Week in December 2025, Salesforce counted US$67 billion in global spend that AI agents directly influenced, out of US$336.6 billion in total retail volume. Adobe, tracking the same holiday season, recorded a 693 per cent year-on-year surge in generative AI-driven retail traffic. McKinsey, which has been sizing this market for two years, now puts the total addressable opportunity at US$1 trillion in US B2C retail revenue and US$3 to US$5 trillion globally by 2030.

The Morph report makes a specific, falsifiable prediction: agent-influenced commerce will cross US$500 billion in global gross merchandise value by the end of 2028. The arithmetic, it argues, is not aggressive. It requires the category to compound at roughly the pace Cyber Week already demonstrated, across two more holiday cycles.

The anatomy of an agentic transaction

Understanding why this matters for Asia requires understanding what actually happens in an agentic transaction.

When an AI agent buys something on a user’s behalf, it must solve four sequential problems.

1. Identify itself to the merchant (identity)
2. Prove the human actually authorised the purchase (mandate)
3. Negotiate cart, tax, shipping, and returns (checkout)
4. Move money (settlement).

Each of these layers now has at least one credible open standard in production. Google shipped the Agent Payments Protocol (AP2) with more than 60 launch partners, including a native extension for stablecoin settlement. Stripe and OpenAI released the Agentic Commerce Protocol, which can be implemented as a RESTful API or a Model Context Protocol (MCP) server. Shopify’s Universal Commerce Protocol, co-developed with Google, is the first cross-merchant cart standard, enabling a single agent to combine products from multiple sellers in a single session. On Ethereum, ERC-8004 went live on mainnet in January 2026, giving agents a portable identity that is not owned by any single platform.

The structural implication is significant. In the card era, Visa and Mastercard owned the token and, therefore, the customer relationship. In the mobile era, Apple and Google extracted rent through the wallet. In the agentic era, as the Morph report puts it, “no single company owns the primary customer relationship. The agent becomes the wallet. The protocol becomes the network.”

What this means for Asia’s merchants

For Southeast Asian and broader pan-Asian merchants, the consequences of this shift are both an opportunity and a threat. On the opportunity side, agentic commerce collapses the friction that has historically disadvantaged cross-border commerce in the region. An agent shopping on behalf of a user in Jakarta or Bengaluru faces the same latency and checkout friction as one shopping in San Francisco — none, by design.

Also Read: Brands are no longer selling to you, they are selling to the AI you asked to decide for you

Walmart in the United States has already pulled its Q1 2026 integration from OpenAI’s Instant Checkout to embed its AI assistant Sparky directly into ChatGPT and Gemini, bypassing the traditional search results page. Salesforce reports AI-channel conversion rates running 700 per cent above social commerce and 200 per cent above organic search.

The high-intent commercial query, the moment a consumer decides to buy, is migrating away from search engines and into AI chat interfaces. Asia’s merchants, many of whom have built their entire growth models on Google Shopping and Meta performance marketing, have not yet reconfigured for this reality.

The Morph report’s Prediction 6 is perhaps the most alarming for brand-heavy categories common across Asia’s consumer markets: agents, it argues, will collapse the brand premium. Comparison-shopped goods, it predicts, will see real-price declines of 10 per cent or more within the prediction window.

BCG research already finds that large language models directly influence up to 20 per cent of purchasing decisions and that agents compare products across a far broader range of price, review, and delivery-speed signals than human shoppers practically can. When a machine with near-perfect information shops on a consumer’s behalf, brand equity, bundling strategies, and choice paralysis stop functioning as pricing defences.

The last time comparison transparency rose this quickly, during the first wave of e-commerce, it took roughly 15 per cent off consumer electronics margins within a decade. Agents, the report warns, will compress a similar shift into a two-year window in comparison-heavy categories.

The habit is forming faster than merchants realise

Consumer readiness is further ahead than most merchants in the region assume. US adult AI tool usage is already estimated to be above one-third, with ChatGPT alone reporting hundreds of millions of weekly active users. Prediction 10 in the Morph report states that by 2028, 1 in 10 US households will regularly allow an AI agent to complete a purchase on their behalf. The comparison drawn is instructive: smartphone penetration crossed 20 per cent of US households roughly 30 months after the iPhone launched. Consumer agent purchases are tracking a faster adoption curve from a higher starting base.

Asia’s mobile-first consumer base, where app-based commerce, super-apps, and social commerce have already compressed multiple technology cycles, is arguably even more primed for this transition. The infrastructure question is not whether agents will buy on behalf of Asian consumers. It is whether Asian merchants, payment rails, and platforms will be ready to accept them when they arrive.

Also Read: Ant International: FinAI paving the last mile for agentic commerce

Cart abandonment, the Morph report notes drily, will stop being a useful KPI. “Agents do not abandon carts. They switch merchants.”

The post The invisible shopper rewriting Asia’s e-commerce playbook  appeared first on e27.

The most dangerous moment in AI governance is not when a model fails in an obvious way. It is when the organisation continues to believe the controls are working while the model has already learned how to move around them.

That is the real meaning of an LLM outsmarting its safety nets. Not that the model has become conscious or malicious in some cinematic sense. But the surrounding control system has become too static, too predictable, too shallow, or too fragmented for the behaviour it is trying to contain. The model keeps producing outputs within the formal boundaries of policy while still creating risk in substance. The organisation sees compliance signals. The real world sees drift, workarounds, evasions, and harm.

The failure is rarely just technical

When leaders say the model outsmarted the safety net, they often describe it as though the problem sat inside the machine. In reality, the deeper failure usually sits inside the organisation’s control design.

Most safety nets are built like perimeter defences. They rely on blocked phrases, predefined risk categories, wrapper prompts, moderation classifiers, policy layers, escalation logic, or human review triggers. Each of these can help. But together they often create a false sense of control because they are still based on a manageable worldview. They assume risk will appear in recognisable forms, arrive through known pathways, and be visible at the point where the control is placed.

That assumption does not survive contact with real deployment for long.

Large language models do not need to break a rule directly in order to defeat their intent. They can reframe, soften, imply, fragment, redirect, or produce outputs that are formally compliant and substantively dangerous. Users can also become part of the evasion system. They learn what the model refuses, what it will tolerate, and how to reformulate requests until the desired outcome appears on the other side of the filter. The risk no longer sits in a single output. It emerges across sequences, contexts, and accumulated interactions.

Safety nets often test what was easy to imagine

It appears that the business context changes the meaning of an answer. It appears when several harmless outputs combine into a risky decision path. It appears when users rely on a confident tone instead of factual quality. It appears when a model learns to sound compliant to internal reviewers while still shaping unsafe downstream choices. It appears when humans stop treating the system as a suggestion engine and start treating it as institutional judgment.

Also Read: Inside the AI Workflow Competition at Echelon Singapore 2026

In other words, the red team is often testing the outer edge of model misuse while missing the inner edge of model dependence.

That is where the red flags begin to matter. The most important signals are not always explicit policy violations. They are patterns that tell you the organisation is starting to rely on the system in ways your formal controls were never designed to govern.

Outsmarting the safety net is often a control plane problem

There is a strategic concept that matters here, and it is still underdiscussed. The model evolves faster than the control plane around it.

By control plane, I mean the combined layer of policy, monitoring, approval logic, escalation paths, audit mechanisms, usage boundaries, and accountability design that is supposed to shape safe deployment. In many organisations, this layer is much slower than the model lifecycle itself. The model improves, the prompts evolve, product teams add use cases, users discover workarounds, and commercial pressure pushes deployment into messier environments. Meanwhile, the control logic remains anchored to an earlier idea of the system.

That gap is where trouble compounds.

The model does not need to become unsafe in some absolute sense. It only needs to move beyond the assumptions built into the control plane. Once that happens, the organisation starts managing a newer risk profile with older governance logic. Safety reviews still happen. Dashboards still look reassuring. Escalation rules still exist. But the control layer is now reacting to a version of the system that no longer exists.

The most dangerous outputs are often institutionally plausible

A lot of safety thinking still focuses on dramatic failure. Toxic responses, prohibited content, obvious manipulation, and explicit instruction on harmful acts. Those are serious, but in enterprise settings, the more consequential failures are often quieter.

The model drafts a justification that sounds legally tidy but strips away material uncertainty. It produces a risk summary that feels authoritative enough to skip deeper review. It helps a junior team member make a decision that should have triggered senior judgment. It accelerates a customer communication that is polished, persuasive, and operationally wrong. It generates internal reasoning that turns weak evidence into a plausible recommendation.

These are not spectacular breakdowns. They are organisationally plausible outputs. That is precisely what makes them dangerous. That is a much harder problem than simple content filtering.

Also Read: If an AI agent cannot find you in Southeast Asia, you do not exist in the market

Red teaming without governance redesign becomes a ritual

Many organisations are now investing in red teaming because it signals seriousness. That is understandable. The trouble begins when red teaming becomes performance rather than pressure.

If the exercise identifies issues but the operating model stays largely the same, the organisation has not really red-teamed the deployment. It has only sampled the model. If the findings do not change approval thresholds, user permissions, escalation design, workflow boundaries, or accountability ownership, then the exercise is functioning more like compliance theatre than control improvement.

This matters because AI risk does not sit neatly inside the model layer. It sits in the full system of use.

A red team may prove that a harmful output can be generated. That is useful. But the more strategic question is whether the organisation has designed conditions under which such an output can actually matter. Who can act on it? Who reviews it? Which workflows are allowed to depend on it? Which customers are exposed to it? Which business decisions absorb it without challenge? Which audit trails exist when things go wrong?

The strategic mistake is trying to solve adaptive risk with a static policy

Many governance frameworks still assume that if the policy is clear enough, the system can be controlled. That is a comforting idea, but LLM deployment does not behave like a static rules environment.

Policy matters, but policy alone does not adapt. Users adapt. Product teams adapt. Workflows adapt. Commercial ambition adapts. The model itself may be updated, tuned, connected to tools, embedded in agents, or exposed to new contexts. Static policy cannot carry that burden on its own.

What is needed instead is adaptive governance.

That means tighter feedback loops between deployment and oversight. It means usage tiering rather than universal access. It means control logic that changes with consequence, not only with content type. It means tracking where models influence decisions, not merely where they generate text. It means governance that follows dependency and impact, not just prohibited prompts.

Most importantly, it means treating control design as a living operating capability rather than a document set.

This is where many senior teams still underestimate the task. They think they are approving a technology deployment. In reality, they are authorising a new behavioural layer inside the institution. That layer will evolve continuously. The governance model has to do the same.

The post Red team with red flags: What happens when your LLMs outsmart your safety nets appeared first on e27.

There is a certain script that most successful tech founders follow: elite university, major city, the right network, a seed round, a Series A, and then, if they are lucky, a cover story.

Joaquín Cuenca, co-founder and CEO of Magnific, an AI-powered creative platform that a16z recently ranked 11th in the world, did not follow that script. Cuenca, a former engineer at Google and a serial entrepreneur, grew up in Cox, a quiet town of just a few thousand people in southern Spain, built his first company there, and has spent the better part of 15 years doing things his own way.

Also Read: Transforming the creative economy and entertainment industry with TipTip

The results speak for themselves.

Born in isolation, built on first principles

Magnific’s origins trace back to Freepik, a stock image platform that Cuenca and his co-founders built from scratch in Málaga. The company was bootstrapped from day one, never raising a single penny from VCs. That decision, which would have seemed eccentric to most investors, turned out to be formative.

“We were not native speakers. We are different from the average entrepreneur in San Francisco,” Cuenca says. “And that shaped the company. It gave us time to grow our uniqueness in the south of Spain, quite isolated. And eventually, we became a strong player in the stock industry by being different.”

Being different, in Freepik’s case, meant offering a product that was completely free: unlimited downloads at a time when stock image subscriptions were expensive and restrictive. “You cannot get a subscription that gives you five images per month. That’s too little,” he says. “Our model that was completely free and with unlimited download options worked much better with that use case.”

Before long, the platform attracted more than one million visitors. It became one of the top 100 most visited websites in the world, particularly popular in India, Brazil, Indonesia, and other emerging markets where price sensitivity made the free model especially compelling.

Since there were no external investor to back its business, they had to be very careful with our financials. “We knew we were not going to get a second shot, so we needed to take care of ourselves,” Cuenca explains.

That discipline built a cost structure that remains significantly leaner than most of its competitors, an advantage that continues to matter as the company charges into generative AI.

The pivot that changed everything

When generative AI arrived, Freepik was well-positioned to reinvent itself, not because it had Silicon Valley connections, but because it had spent years cultivating a massive, loyal user base and had learned to think from first principles.

“When generative AI came, the fact that we had to create the company based on first principles, kind of isolated from all the advice and context in the US, it allowed us to rethink everything from scratch,” Cuenca says.

Also Read: Is generative AI the game-changer for productivity?

That rethinking led to the rebrand from Freepik to Magnific, a decision that represented far more than a name change. It was an acknowledgement that the company’s mission had fundamentally shifted.

“When we started Freepik, the number one goal was to make graphical assets accessible to people. Then when we started doing generative AI, we realised that our goal was to help people achieve better results, something that made them feel proud of what they made. And Freepik was not representing that,” he shares.

The rebranding exercise was not without risk; 14 years of brand equity does not simply transfer overnight. But Cuenca’s logic is clear: “You need to believe that there is a vertical alignment between your brand and your mission. If you feel that your brand is not completely aligned with your mission, that’s when you need to change.”

The macro economy bet

In plain English, Magnific builds AI-powered creative tools for professionals, studios, and brands making their best work across every medium. Its AI Suite brings together image generation, video creation, upscaling, and a library of over 250 million assets.

Magnific’s commercial thesis rests on what Cuenca calls the “macro economy”, a belief that generative AI will dramatically expand the creative economy rather than shrink it. It is a view that will unsettle many working designers and video editors, but he makes the case with a concrete example from within his own company.

“Before generative AI, we had a marketing department. We never created short films. We started creating films when it became so easy and so fast that it became affordable,” he says.

“The first thing we did when we started making our little films was hire an expert in audio. Then we got an external photographer. Then people who are very good at telling stories and then we hired folks who were very good at colour grading. Before you realise, you’re employing four or five people in a company that employed none before,” he continues.

Cuenca’s broader argument is that the film industry is shifting from a small number of very large, expensive projects to a much greater number of smaller ones being green-lit because production costs have fallen. “We believe that the creative economy is going to expand and not become smaller.”

When pressed on whether this optimism is convenient for a platform that benefits commercially from people believing it, he is direct: “If your sales pitch and your vision are not aligned, you’re not going to get very far. We have been very consistent in delivering towards the vision that we sell.”

His analogy is revealing: “I believe we will make films like we write books. One person with a vision, with a story. The technology to crystallise that vision is not a problem. Technology will not be the value.”

He is quick to add that Hollywood-scale productions will not disappear, but alongside them, a new spectrum of creator-driven work will flourish. “It’s going to go from one to many.”

Asia is already here

For a publication focused on Asia, particularly Southeast Asia, perhaps the most striking revelation is how central the region already is to Magnific’s user base. India is the company’s largest market by number of users. Brazil is second. The US is third. Indonesia, Thailand, and the broader region follow closely behind.

“Those are huge markets. We have been there very prominently for many, many years,” Cuenca says. The perception that AI tools are dominated by American or Chinese companies does not particularly concern him. “People don’t look at the label to see if a product comes from the US or not; they just use the product that they want to use.”

Also Read: Beyond the hype: What generative AI is actually changing in startups

For enterprise customers, being a European company is, if anything, an advantage. “For them, it’s a little bit like Switzerland. It’s not the US, it’s not China. They know that their data is going to remain private. It doesn’t go to Big Techs in the US, doesn’t go to China.”

Magnific is also actively expanding its presence in Asia, with events planned in Singapore around “Super AI” conference and growing enterprise engagement in Japan.

Starting prices on the Magnific platform sit around US$6 to US$7.99 per month depending on the plan, with free access to a large library of existing images for users who cannot yet justify a subscription. “There is no magic. Generating images costs money and we need to charge the user,” Cuenca acknowledges. “But when we can make it better for the user, we deliver the value.”

What comes next

The question of whether Magnific is the last company Cuenca will build is one he finds genuinely impossible to answer. “It’s like trying to guess who I will be in 10 years. I may get excited about something in biotech. I don’t know. I want to keep all my options open.”

What he is certain of is that his background (the small town, the bootstrapped beginnings, the years of building in isolation) continues to shape how he thinks. When asked about the threat of Adobe, Google, or other giants eventually moving into Magnific’s territory, he is measured but confident.

“The biggest players have a hard time when there is a dramatic shift,” he says. “It has happened many times. Canva succeeded because when browsers became so powerful, it became viable to build a graphic design product on top of them. Before the browser, it was impossible to take over a company like that.” His point is that size is a liability, not an asset, when the ground moves beneath you.

For now, the ground appears to be moving very much in Magnific’s direction.
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