Magnific co-founder and CEO Joaquín Cuenca

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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The AI talent gap is usually described as a skills gap.

I think that is only partly right.

The deeper gap is between people who are learning AI as a real way to change work, and people who are only being taught AI as a course, a tool, or a slogan.

That difference matters.

Some AI upskilling may be making the already-advantaged look more ready, while doing very little for the people closest to the work.

That is not because the training is useless. It is because too much of it stops at awareness, access, or generic tool practice.

In product, software, digital, operations, and business roles, the workplace is not going to ask people whether they completed an AI module. It is going to ask whether they can understand a problem, break it down, use the right tools responsibly, deliver something useful, and explain the value created.

That is a much higher bar than AI awareness.

It is also a more honest one.

The problem with template readiness

I have seen this gap show up in student and early-career portfolios.

Many people have built some version of the same project: a classifier, a tutorial chatbot, a todo app, a dashboard, a standard exercise from an online coding course.

There is nothing wrong with these as first projects. Everyone needs a starting point.

But when too many portfolios show the same pattern, the project stops being evidence of problem-solving. It becomes evidence of template completion.

That is not AI readiness.

AI-ready talent is not someone who can repeat a demo. It is someone who can find a real friction point, decide whether AI is useful, build a workflow around it, test it, learn from what happens, and explain what changed.

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

The useful question is not, “Did you build an AI project?”

It is, “What real problem made you reach for AI in the first place?”

That question changes the quality of the work.

It pushes students and workers away from generic demonstrations and toward actual delivery. It asks them to think about users, data, edge cases, errors, privacy, false confidence, adoption, and whether the work creates value outside the assignment.

Schools and workplaces are sending mixed signals

One reason the pipeline struggles is that education and work are not always aligned.

Some students are still told not to use AI in school projects. I understand the concern. Educators are trying to protect learning, originality, and assessment integrity.

But a blanket ban creates a strange divide.

The workplace that those students are entering will not ask them to behave as if AI does not exist. It will ask whether they can use it well.

That means education has to move from prohibition to disclosure and judgment.

Instead of asking only, “Did you use AI?”, schools can ask better questions:

• What did you ask AI to do?
• What did you accept, reject, or rewrite?
• What assumptions did you check?
• What failed in testing?
• What did a human still need to decide?
• What value did the work create?

That is a much stronger assessment than pretending the tool is not there.

It also prepares students for how good teams will actually work.

The quiet divide is about who gets to redesign work

There is another divide that gets less attention.

AI upskilling often reaches the people already closest to formal learning: corporate staff, knowledge workers, managers, technologists, and people who have time, language confidence, device access, and permission to experiment.

But many of the best AI use cases live closer to operational friction.

Think about the admin worker who knows where forms get stuck. The support coordinator who sees the same customer confusion every week. The logistics staff member who knows which updates create delays. The small-business operator who could use AI for customer replies, translation, menu writing, inventory notes, or simple planning, but does not have time for abstract training.

Also Read: AI does not replace people, it reveals who was never truly irreplaceable

These are not marginal examples.

They are where productivity gains often become real.

The risk is that AI becomes something designed above these workers and applied to them, rather than something they are invited to use to improve the work they understand best.

That creates a second divide: not just people who have AI tools and people who do not, but people who get to redesign workflows and people whose workflows are redesigned around them.

If we care about both equity and productivity, that matters.

Course completion is not enough

Governments, companies, and training providers are right to invest in AI capability. In a market like Singapore, there is already serious attention on AI skills, workforce transformation, and national competitiveness.

But the benchmark has to keep evolving.

Counting training seats is useful, but it is not enough.

Course completion does not prove that someone can change a workflow.

Tool access does not prove that a worker has permission to use it.

AI literacy does not prove that a team can adopt AI safely in the messy parts of the business.

This is where I think we are too polite. A market can produce thousands of AI-trained people and still leave the actual work mostly unchanged.

The better benchmark is delivering evidence.

What did the person observe? What problem did they choose? Why was AI the right tool, or not the right tool? What did they build? Who used it? What changed after feedback? What risk did they notice? What value did it create?

That kind of evidence tells an employer more than a certificate or a polished tutorial project.

It also tells policymakers and training providers whether upskilling is reaching real work or staying in the training room.

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Singapore organisations rank among the most rigorous in the Asia-Pacific region when it comes to cybersecurity governance, yet a significant gap persists between policy intent and operational enforcement, according to new findings from JFrog’s 2026 Software Supply Chain Security State of the Union report.

The report, which surveyed 1,508 IT professionals across eight countries — including 174 respondents based in Singapore — paints a nuanced picture of a market that has invested heavily in governance frameworks but lacks the tooling to make them self-enforcing.

The findings arrive against a backdrop of escalating global threats. Malicious packages uploaded to the npm registry surged 451 per cent year-on-year to 171,592, while 495 weaponised AI models were detected on public registries. A further 11.7 million new packages entered software supply chains over the same period.

On several headline measures, Singapore performed well. The country led all eight surveyed nations on network proxy enforcement, with 67 per cent of organisations applying controls at that layer. Additionally, 71 per cent of Singapore respondents said they carefully review AI-suggested code fixes before implementation — the highest rate of AI scrutiny recorded in the dataset.

However, those strengths are offset by a series of structural vulnerabilities.

Also Read: Why Singapore’s deep tech founders need more than good science — and how National GRIP is filling that gap

Audit readiness emerged as a particular concern. While 95 per cent of Singapore organisations claim to track application ownership, 54 per cent said they would need a week or more to produce compliance documentation for a single application on demand, suggesting that data exists in principle but is not structured for rapid retrieval.

Open-source software approval processes also lag the region. Some 59 per cent of developers in Singapore wait a week or more for new package approvals to be granted, the slowest rate recorded across the APAC markets surveyed.

The report also identified a notable blind spot around so-called “shadow AI tools”. Eighteen per cent of Singapore organisations have formal policies prohibiting the use of unauthorised AI tools, yet have no technical mechanism to detect when those policies are violated — the highest “policy-only” rate in APAC.

Secrets detection, a control designed to identify exposed credentials and API keys embedded in code, remains significantly underdeployed. Only 25 per cent of Singapore organisations have adopted the capability, a figure broadly in line with the global average of 28 per cent.

Human review cannot match development speed

The report highlights the operational strain created by relying on manual processes to govern AI-accelerated development workflows. Sixty per cent of Singapore DevSecOps stakeholders identified security governance and policy enforcement as their primary time burden, while 41 per cent cited the review and hardening of AI-generated code as a significant drain on resources.

Also Read: The talent reset: Why AI is changing what makes people valuable

Sunny Rao, senior vice president of Asia-Pacific at JFrog, said the findings reflected a common transition point for mature markets.

“Singapore has done a lot of hard work in building governance frameworks that most markets are still debating,” Rao said. “Policies that rely on manual review and human checkpoints cannot keep up with AI-driven development. The organisations that will lead from here are the ones that embed enforcement directly into the pipeline — so that every artefact, every model, and every dependency is curated, scanned, and validated before it ever reaches a developer’s machine.”

JFrog’s report points to automated, platform-level enforcement as the recommended path forward — including pre-vetted package curation, automated secrets scanning, and contextual vulnerability analysis to prioritise remediation efforts based on actual deployment environments.

The full report is drawn from JFrog’s global survey of 1,508 IT professionals conducted across eight countries in 2026.

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Bitcoin faces intense downward pressure, tumbling over 6.09 per cent in a 24-hour window to US$66,867.60. This sharp correction means the premier digital asset is notably underperforming the broader cryptocurrency market, which itself fell by 5.39 per cent over the same period.

The velocity of this descent suggests that a complex interplay of excessive leverage, cooling institutional appetite, and structural liquidations has fundamentally transformed what could have been a standard market correction into a disorderly retreat. For observers tracking these capital flows, this pronounced vulnerability highlights how deeply the cryptocurrency ecosystem remains bound to sudden changes in market sentiment and leveraged positioning.

At the very core of this steep price drop sits a massive derivatives-driven liquidation cascade that completely transformed the market dynamics over a 24-hour period. Leveraged long positions worth nearly US$789 million were completely wiped out, forcing automated and non-discretionary market selling that triggered a painful feedback loop. This volume of liquidations represents an astonishing 172 per cent surge over the prior day, proving that retail and institutional traders alike were positioned far too aggressively on the long side.

As prices breached psychological support levels at US$70,000 and US$68,000, exchange liquidation engines automatically dumped collateral into an increasingly illiquid spot market, thereby amplifying the decline’s velocity. Market stabilisation now depends entirely on whether funding rates and open interest can stabilise, signalling that this aggressive excess leverage has been thoroughly cleared from the system.

Also Read: Bitcoin down 3.32% as US$283M in liquidations wipe out leveraged traders: Saylor’s power?

Compounding this structural selling pressure is a visible erosion of institutional confidence, a pillar that many believed would permanently anchor prices throughout the year. For the 11th consecutive day, spot Bitcoin exchange-traded funds registered persistent capital withdrawals, with total aggregate outflows reaching US$3.45 billion.

This prolonged streak of capital flight indicates a broader risk-off rotation as professional allocators quietly shift their capital out of digital assets and reallocate it directly into outperforming traditional equities, with artificial intelligence stocks attracting the vast majority of this liquidity.

Furthermore, sentiment suffered a sharp psychological shock following reports that MicroStrategy executed its first Bitcoin sale since 2022. Even though the transaction was minor, it shattered the firmly held market narrative that the corporate treasury would exclusively accumulate and never sell its holdings, introducing an element of doubt that further spooked already nervous participants.

Bitcoin has officially pushed deep into oversold territory, with its 14-day relative strength index collapsing down to 29.09. The digital asset is currently testing a critical floor between its recent swing low of US$66,127 and the 78.6 per cent Fibonacci retracement level located at US$67,300. If buyers fail to defend this crucial US$66,127 mark, the structural bearishness will likely intensify and open up a direct path toward US$64,000.

Conversely, if exchange-traded fund outflows finally begin to slow or turn neutral today, a successful defence of this support zone could easily spark a quick relief rally back toward the 50 per cent Fibonacci level at US$68,868.

Also Read: Why US$73,000 is the most important Bitcoin level right now

The current assessment points to a market dominated by strong bearish momentum, where the combination of aggressive liquidations and a cooling institutional bid has firmly handed control over to the sellers. While deeply oversold conditions frequently precede a sharp technical bounce, any near-term recovery will likely remain incredibly weak and highly vulnerable until the asset reclaims and stabilises above its key overhead resistance zones.

Risk managers must keep a vigilant eye on today’s exchange-traded fund data to see if institutional selling pressure is showing signs of exhaustion, while simultaneously watching whether the spot price can successfully defend its current support lines.

Evaluating the probabilities for how this market structure will evolve over the coming days reveals three distinct tactical scenarios for risk allocators to monitor. The primary scenario carries a 65 per cent probability and envisions Bitcoin staging a modest technical bounce from its current oversold conditions to retest US$68,868, before ultimately succumbing to the overarching bearish trend and resuming its decline toward US$64,000. There is a secondary 25 per cent probability that the selling pressure has already peaked, which would allow the asset to firmly establish a long-term bottom right here and begin a sustained, grinding recovery that targets a full reclaim of US$70,000.

Finally, a minor 10 per cent probability exists for an immediate, catastrophic continuation of the liquidation event, a worst-case scenario that would bypass any intermediate consolidation and plunge the asset straight through US$64,000 down to deeper macro support levels.

Let’s see.

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The post A 65% probability explains the next likely move for Bitcoin as leverage clears appeared first on e27.

Cool Japan Fund’s latest disbursement into Jakarta-based vending operator JumpStart has propelled the company from Series B to Series C, reinforcing a bet on automated retail as a conduit for Japanese F&B brands into Indonesia, and by extension, Southeast Asia.

The undisclosed follow-on investment from CJF comes as JumpStart reports rapid expansion of its machine network and remarkable year-on-year revenue gains. The company said it operated more than 6,500 vending machines in Indonesia at the end of 2025 and logged financial growth of up to 200 per cent compared with the previous year.

Also Read: What Japan and Southeast Asia teach us about co-creating innovation

A distribution platform, not just machines, JumpStart positions itself as more than a vending-machine operator. Its strategy is to build a scalable distribution platform that connects international, primarily Japanese, brands with Indonesian consumers via modern retail infrastructure.

CEO Brian Imawan framed the funding as validation of that vision. “The continued support from the Cool Japan Fund is a strong validation of our vision and execution. We are not only building a vending machine network, but also creating a scalable distribution platform to connect global brands, especially from Japan, with the growing Indonesian consumer market,” Imawan said in a statement.

That language is telling: the company is pitching automated retail as both a logistics channel and a marketing front. Machines are placed in high-footfall settings (office towers, transit hubs, universities, and retail precincts) and equipped with AI and cashless payments to enable dynamic merchandising and targeted promotions.

Tech and cashless payments as growth levers

JumpStart’s machines deploy artificial intelligence to optimise inventory decisions and personalise the consumer experience, the company says. Combined with cashless payment systems, the platform can gather transaction data and customer insights that are scarce in many parts of Indonesia’s fragmented retail landscape.

This data-driven approach is part of a wider trend in the region where retailers, digital platforms and payment networks are converging. For international brands seeking market entry, access to local consumer behaviour and real-time performance metrics reduces uncertainty. In markets like Indonesia, where e-wallets and digital payments have leapfrogged in many urban areas, cashless vending reduces friction and expands monetisation opportunities beyond single transactions, for example, through loyalty programmes and push promotions.

Why Cool Japan is doubling down

For CJF, a government-supported vehicle created to promote Japanese culture and industries abroad, the rationale is two-fold. First, Indonesia is a critical consumer market in Southeast Asia: its population exceeds 275 million, and urban middle-class consumption is growing. Second, vending machines offering Japanese snacks, beverages and convenience items are a soft-power instrument, a familiar way to introduce Japanese brands, flavours and lifestyles to a foreign audience.

Also Read: East meets Southeast: How Japan can empower a new wave of SEA startup innovation

CJF’s investment therefore serves both commercial and cultural aims: it accelerates market access for Japanese SMEs and strengthens bilateral cultural ties through everyday consumption.

Operational priorities and challenges

JumpStart intends to use the new capital to expand its machine footprint, broaden its product variety, especially through partnerships with Japanese suppliers, and reinforce its operational infrastructure and supply chains. That mirrors common scaling priorities for automated retail players: site acquisition, restocking logistics, cold-chain management for chilled items, and machine maintenance.

However, scaling a vending network in Indonesia presents specific challenges. The archipelagic geography complicates logistics; ensuring timely restocking and refrigeration across islands increases costs. Site selection remains a labour-intensive process that requires local relationships and negotiation with property owners. Machines also face vandalism and theft risks in some locations, pushing operators to invest in surveillance, sturdier hardware and insurance.

Moreover, the sustainability of growth will depend on the economics of each machine: average revenue per machine must justify hardware, installation, and ongoing operational expenses. The 200 per cent year-on-year growth headline is striking, but sustaining high growth rates as the base expands is statistically harder.

A Southeast Asian playbook

While the current focus is on Indonesia, the broader implication is regional. If JumpStart can prove unit economics at scale and develop reliable supply chains, the model could be replicated across Southeast Asia, especially in neighbouring markets with similar consumer profiles and rising digital payments, such as the Philippines, Vietnam and Thailand.

For Japanese brands, a regional vending network offers a controlled, low-barrier entry point before committing to full retail distribution deals. For JumpStart, the value proposition is to act as both distributor and experiential marketer, a company that can place a product in front of millions of consumers while delivering data on how it performs.

What this means for incumbents and investors

The deal signals investor appetite for niche, tech-enabled retail infrastructure that combines hardware, software and cross-border partnerships. It also raises the bar for incumbents: traditional distributors, convenience store chains, and local FMCG players may feel pressure to match the immediacy and data feedback loop that vending platforms promise.

For investors, the opportunity hinges on the operator’s ability to scale efficiently and to translate user data into higher-margin services, such as targeted advertising, subscription models, or white-label distribution for international brands.

An important route for Japanese brands

CJF’s move to back JumpStart further underlines the convergence of trade, culture and technology in Southeast Asia’s retail sector. The investment is a concrete example of how soft-power funds can catalyse commercial ventures that export culture through consumer goods, and it positions automated vending as a potentially important route for Japanese brands to reach fast-growing consumers in Indonesia and the wider region.

Also Read: “SEA + Japan is a long game”: MUIP’s Gerrard Lai on cross-border startup collaboration

Whether JumpStart can sustain its growth while solving the logistical and economic puzzles of large-scale vending across an archipelago remains the key question. But for now, the deal marks a notable vote of confidence in automated retail’s role in Southeast Asia’s evolving consumer landscape.

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SG Enviro, a Singapore-headquartered industrial water and wastewater engineering firm, has closed a Series A round led by Emerald Ventures with co-investment from SEEDS, the investment arm of SG Growth Capital.

The funding will accelerate the company’s expansion across Southeast Asia and deepen its presence in Singapore, Malaysia, and Indonesia, with plans to enter Thailand and target high-growth sectors such as data centres, semiconductors and pharmaceuticals.

Also Read: SG Enviro bags US$5.92M to tackle Southeast Asia’s wastewater challenge

The raise comes as regional manufacturers and heavy industries face tightening regulations, rising utility costs and growing corporate pressure to reduce water and carbon footprints. For Southeast Asia, where water stress, ageing treatment plants and resource-heavy industries such as palm oil and oil and gas are concentrated, the timing is significant.

From retrofits to new utility stacks

SG Enviro, founded in 2018, positions itself as a full lifecycle partner: engineering, procurement and construction (EPC), plus operations and maintenance (O&M). Its stated strength is retrofitting and upgrading legacy wastewater infrastructure, an approach that can extend asset life and reduce both environmental impact and operating costs.

Retrofitting older plants is a pragmatic play in the region. Many industrial wastewater systems across the region were designed when environmental expectations were lower; upgrading these systems can deliver immediate compliance and operational gains without the capital outlay of greenfield projects. For industries such as palm oil, food and beverage, and oil and gas –where effluent composition and volumes vary widely — tailored technical solutions are often required, a fact SG Enviro highlights as a competitive advantage.

Targeting higher-margin, high-demand sectors

Beyond traditional industries, SG Enviro is explicitly moving to address the utilities needs of data centres, semiconductor fabs and pharmaceutical manufacturers. These sectors are both water‑intensive and increasingly bound by strict environmental and reliability standards.

Also Read: Innovating for impact: A better solution for household water treatment

For Singapore and Malaysia, countries actively courting hyperscale data centre investment, specialist wastewater and water-reuse solutions can be a differentiator for sites seeking long-term operating licences and investor favour.

Dr Helge Daebel, head of its water practice at Emerald, said. “SG Enviro’s advanced wastewater treatment expertise plays an important role in supporting Singapore’s growing industrial sector while driving its regional expansion. He described SEEDS as a partner with “strong local support capabilities and extensive Southeast Asian reach” that can add value to SG Enviro’s growth.

Investor backing and regional strategy

Emerald Technology Ventures is a global venture firm with a long track record in climate and sustainability-related investments. The firm manages and advises assets totalling over €1 billion (~US$1.1 billion) across offices in Zurich, Toronto and Singapore. SEEDS, operating under SG Growth Capital, co-invests to catalyse private capital into Singapore-based technology startups with global ambitions.

Christine Giam, partner at SG Growth Capital, reinforced the Singapore-to-region narrative: “Leveraging Singapore’s strengths as a hub for engineering innovation and industrial expertise, SG Enviro combines water treatment technologies with deep execution capabilities to address complex industrial wastewater challenges.” Her comment underscores a common playbook: use Singapore as a launchpad for regional scaling, blending engineering credentials with local market partnerships.

Commercial realities and competitive landscape

While the market opportunity in Southeast Asia is clear, competition is intense. Multinational EPC firms, established regional engineering houses and emerging specialist technology providers all compete for industrial water projects. SG Enviro’s focus on retrofits and on-the-ground O&M aims to capture recurring revenue streams that pure equipment providers may miss. Securing long-term service contracts with industrial clients would bolster margins and create stickier customer relationships.

Another practical lever for growth will be strategic partnerships. The company said it plans to use part of the new capital to strengthen its talent pipeline and form alliances—moves that could ease market entry in countries where local content, licences and sector connections matter.

Regulation and corporate sustainability as tailwinds

Southeast Asian governments are increasingly tightening industrial effluent and discharge standards and nudging industries towards circular resource use. At the same time, multinational buyers and lenders are pushing suppliers to demonstrate environmental performance across scope 1–3 emissions and water stewardship policies. These dual pressures make wastewater treatment not just an environmental obligation but a business continuity and finance-enabling service.

For example, palm oil mills and food processors that can demonstrate water reuse and energy recovery through anaerobic digestion can improve margins and access export markets subject to stricter sustainability requirements. Data centres seeking reliability assurances in water-scarce jurisdictions need resilient, fit-for-purpose utilities—an addressable niche for specialist firms.

What to watch next

Investors and clients will be watching several indicators: whether SG Enviro can win larger EPC contracts outside Singapore, the pace of O&M contract wins that deliver recurring revenue, and its ability to form local partnerships in Malaysia, Indonesia and Thailand. The company’s move into highly regulated sectors such as semiconductors and pharmaceuticals will demand both technical depth and compliance credentials.

Also Read: Hydroleap revolutionises wastewater treatment, leading industries into a sustainable future

With Southeast Asia’s industrial base transitioning under regulatory and commercial pressures, there is clear demand for firms that can combine technology with execution. SG Enviro’s Series A gives it financial headroom to test whether that combination can scale across the region.

Possible near-term milestones to look for include announcements of anchor client contracts in the new sectors, local joint ventures or partnerships in Indonesia and Thailand, and the hiring of senior country leads to accelerate sales and project delivery.

The post SG Enviro closes Series A, targets water- and energy-hungry data centres and fabs in SEA appeared first on e27.

For years, the conversation around AI has revolved around one question: Will AI replace jobs?

It is understandable. Change creates uncertainty, and few technologies have moved as quickly or visibly as artificial intelligence.

But this may be the wrong question. The more interesting one is: how does AI change the structure of work itself?

From what I am seeing as a founder building and working alongside AI systems, AI is not simply replacing teams. It is redesigning how work is organised, delegated, and executed. The future workforce may not be defined by company size or headcount alone. Instead, it may increasingly be shaped by how effectively humans collaborate with AI. And that collaboration may look less like software and more like teammates.

Leverage existed before AI — AI made it conversational

Before AI became mainstream, businesses were already using systems and technology to create leverage. We were highly workflow-driven long before AI entered the picture. Processes, systems, and automation were already central to how we operated.

But traditional automation still required technical setup and rigid logic. AI changed something fundamental. It made conversational leverage. Instead of navigating complex systems or relying entirely on human execution, work could increasingly be delegated through natural language.

That shift matters. Because AI did not invent operational leverage. It made leverage more accessible, adaptive, and personalised.

This became especially clear while building Seraphina, my AI twin and assistant. Seraphina was not designed as a generic chatbot. It was built around years of workflows, communication patterns, content, and operational context. What emerged was not simply an AI tool. It felt more like an operational companion. And that experience changed how I thought about work.

Sometimes we are not thinking anymore, we are processing

One of the biggest misconceptions around productivity is that humans spend most of their time thinking. In reality, many knowledge workers spend enormous amounts of time processing emails, coordinating, making drafts, making repetitive decisions, and performing endless operational tasks.

At some point, the workload becomes so heavy that creativity and strategic thinking begin to disappear. You are no longer innovating. You are simply reacting.

Also Read: The AI productivity gurus are bluffing too

This is where AI becomes transformative. Not because it eliminates human contribution, but because it redistributes cognitive effort. When repetitive execution shifts toward AI, humans regain space for strategy, judgment, leadership, relationship-building, creativity, systems thinking, and innovation.

That distinction matters. Because productivity is not only about doing more. It is about creating more room to think better. In many organisations today, the real bottleneck is not intelligence. It is processing overload.

The rise of one person + one AI

This is why I increasingly believe we are moving toward a one-person + one AI workforce model.

Just as many employees today have laptops, email accounts, and productivity software, future workers may have something else: their own personal AI teammate. Not merely a chatbot, but an AI layer that understands personal workflows, communication style, task history, operational preferences, contextual memory, and recurring responsibilities.

We describe this as one person + one AI. The purpose is not to remove humans from work. It is to elevate them.

If execution can increasingly be supported by AI, humans gain more time for higher-value contributions. This naturally shifts organisations away from measuring visible busyness and toward something more meaningful: outcomes. Businesses do not survive because they follow a perfect process. They survive because they create meaningful outcomes. AI simply makes outcome-oriented work more achievable.

I recently shared this philosophy with an intern. My expectation was not that she would spend every hour grinding through tasks manually. The AI could generate much of the execution. Her role was to audit, review, and ensure quality before work went live. The goal was not time spent. The goal was responsible output.

One AI is not enough — the future may be AI crews

Personal AI is only one layer. The next evolution is what I call an AI crew.

Many people imagine AI as one super assistant doing everything. I do not think that is how this develops. Because no single human department handles every function, and no single AI should either.

A founder may increasingly work alongside an AI writer, an AI researcher, an AI marketer, an AI operator, an AI developer, and an AI auditor. Not one AI – a crew of specialised systems. This mirrors how organisations already function. Marketing does not replace finance. Operations does not replace legal. Likewise, specialised AI systems trained around particular workflows often perform more effectively than a single general-purpose assistant.

I have noticed this myself. Seraphina is a strong assistant, but she cannot fully audit herself. Strangely, this resembles human behaviour. Humans have blind spots. So do AIs. Which means future AI systems may increasingly work together, checking, validating, and supporting one another. The future may not be one super-intelligence. It may be coordinated intelligence.

Also Read: Building with AI has never been easier, just do not build the next Chegg

AI will replace some roles, and we should be honest about that

Some roles will likely be reduced or reshaped, especially those centred heavily around repetitive execution, coordination, or predictable processing. But technological evolution has always changed execution.

When Photoshop first appeared, design required specialised expertise and significant training. Then the tools became easier. Canva expanded access. The work changed. Human creativity did not disappear. Execution evolved. AI may follow a similar path.

And while some tasks become automated, human value may shift upward – toward judgment, taste, leadership, creativity, emotional intelligence, strategic thinking, and relationship building. People still buy from people. That has not changed. What changes is how much repetitive labour is required before humans can create that value.

Prompting may be a leadership skill

One surprising insight: people who struggle to delegate to AI often struggle to delegate generally. That is not criticism. It is an observation.

Prompting is frequently treated as a technical AI skill. I see it differently. Prompting is often a leadership and delegation skill. If instructions are vague, unclear, or inconsistent, AI performs poorly. Humans do too.

The reason experienced operators may gain disproportionate leverage from AI is not simply because they use better software. It is because they already understand systems, delegation, workflows, briefing, and operational clarity. AI accelerates these strengths. It also exposes weaknesses faster.

This is why AI alone is not the unfair advantage. Experience plus AI is. A new founder has access to the same tools. But founders who spent years building systems, leading teams, and learning through execution may compress timelines dramatically. What once took months can increasingly happen in weeks. Not because the work disappears. But because execution becomes amplified.

Also Read: Faster tech, slower brains: The biological blind spot of the AI race

Human judgment still matters

Despite everything AI can do, it should not replace human judgment, because not every decision is an efficiency problem. Some decisions involve wellbeing, leadership, values, emotional impact, and sustainability.

Recently, my team declined a client opportunity. Not because the work was impossible – AI could likely have helped process it. But we recognised something more important: the engagement would create mental strain and disrupt team balance. AI might have calculated feasibility. Humans considered consequences. That distinction matters.

AI can optimise processes. Humans remain responsible for deciding what deserves their energy. That is leadership. And leadership still requires judgment.

The workforce may be redesigned, not reduced

AI may give us something many people have quietly lost: time. Time to think. Time to build intentionally. Time to create. Time to reconnect with work that feels meaningful.

The future workforce may not be defined by company size or by who has the largest headcount. It may instead be defined by something far more interesting: how intelligently humans and AI collaborate together. Not humans versus AI, but humans, personal AI, and AI crews working alongside one another.

And perhaps that is the real shift worth paying attention to.

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Southeast Asia’s gaming ecosystem is staring at a US$14 billion projection by 2030, and the danger is not that the number is wrong. The danger is that the industry treats it as inevitable.

The Ampverse report deserves credit for surfacing the structural fault lines beneath the headline figure: cultural fragmentation across six operationally distinct markets, measurement frameworks still unfit for brand investment at scale, e-sports economics that remain commercially unproven, and localisation demands that currently favour only the largest global players. These are not temporary frictions. They are the architecture of the problem.

The most telling detail in the report is the gap between US$7.1 billion in direct gaming revenue and US$14 billion in full ecosystem value. Bridging that gap requires creator monetisation, brand advertising, and e-sports to mature simultaneously, across six countries, on an aggressive timeline. Each condition is achievable. None is guaranteed.

What the projection actually maps is not a destination but a construction brief. The infrastructure companies — measurement platforms, localisation tooling, sustainable live-event formats — are not supporting actors in this story. They are the precondition for it. Investors who understand that distinction will be better positioned than those chasing the headline number alone.

The post Ecosystem Roundup: The US$14B bet SEA gaming can’t afford to sleepwalk into appeared first on e27.

The financial markets presented a striking dichotomy as June began, with traditional equities soaring to unprecedented heights while Bitcoin stumbled under the weight of institutional exodus. This divergence tells a compelling story about where smart money flows when uncertainty meets opportunity and reveals much about the current state of investor confidence across asset classes.

Wall Street celebrated its fourth consecutive day of record closes, with all three major indices finishing higher. The S&P 500 reached 7,599.96, gaining 19.90 points or 0.26 per cent. The Nasdaq Composite proved particularly strong, climbing 114.19 points to settle at 27,086.81, representing a 0.42 per cent increase. Even the more conservative Dow Jones Industrial Average managed to eke out gains, rising 46.42 points to 51,078.88, though its 0.09 per cent advance showed more modest enthusiasm. This sustained rally reflects growing confidence in technology sector momentum and easing geopolitical tensions.

The catalyst behind this equity euphoria stems largely from developments in artificial intelligence. NVIDIA CEO Jensen Huang unveiled the RTX Spark Superchip at the Computex conference, sending shockwaves through the technology sector. NVIDIA itself surged 6.26 per cent on the announcement, while partners and beneficiaries rode the wave higher. Dell Technologies jumped 11 per cent, Oracle gained 9.9 per cent, and Micron Technology climbed 6.6 per cent to cross the psychologically important US$1,000 per share threshold. Arm Holdings skyrocketed 16 per cent on news of its partnership with Nvidia. This massive AI product release triggered widespread demand for hardware and software, drawing capital into related names with remarkable velocity.

Certain technology companies did not share in this celebration. Qualcomm dropped 8.8 per cent, and Intel lost 4.7 per cent, indicating that investors distinguish between AI leaders and laggards with increasing precision. Salesforce led traditional blue-chip performance with a 9.57 per cent gain, showing that strength extended beyond pure technology plays. The broader market advance occurred despite initial volatility in energy markets, where crude oil futures spiked 8 per cent on Middle East supply concerns. Initial reports that Iran would halt communications caused this volatile oil surge. Sentiment recovered rapidly after President Trump intervened to clarify that diplomatic peace talks with Iran continue, allowing WTI crude to settle near US$92 per barrel, trimming the initial panic spike.

Also Read: Why US$73,000 is the most important Bitcoin level right now

Macroeconomic indicators further supported this bullish equity environment. US factory activity expanded in May for a fifth consecutive month, providing fundamental support for equity valuations. Investors are keeping a close eye on upcoming labour data, starting with the JOLTS job openings report, to gauge the underlying strength of the domestic economy. In the Asia-Pacific region, share markets eased slightly from record highs as regional factors came into play. The Australian S&P/ASX 200 closed virtually flat at -0.03 per cent amid a 4.75 per cent national minimum award wage increase. This global perspective highlights the broad-based nature of the current economic expansion and demonstrates how varied local economic policies influence regional market performance.

Against this backdrop of equity market euphoria, the 3.32 per cent decline of Bitcoin to US$71,168.70 over 24 hours appears particularly stark. The cryptocurrency underperformed not just stocks but also its own recent trajectory, falling to its lowest level since mid-April. This weakness stems from sustained institutional selling pressure that has turned the narrative around digital assets decidedly negative.

The primary culprit behind Bitcoin’s struggles is persistent outflows from US spot Bitcoin ETFs, which have seen nearly US$3 billion in net redemptions over a 10-day streak. This marks the first time in 2026 that year-to-date flows have turned negative, signalling a meaningful shift in institutional appetite. The outflow streak indicates that the same institutional capital that propelled Bitcoin to new heights earlier in the year now rotates toward traditional assets offering clearer fundamental support. This persistent selling pressure removes a key source of buy-side support that had previously stabilised the digital asset during minor market corrections.

Adding symbolic weight to the selling pressure, Strategy executed its first Bitcoin sale since 2022. The firm disposed of 32 BTC for approximately US$2.5 million at an average price of US$77,135 between May 26 and May 31. The company explicitly stated that this transaction aimed to fund distributions on its preferred stock. While the transaction size proves immaterial relative to the massive crypto market, the psychological impact resonated loudly. Michael Saylor’s company had built its reputation on an unwavering accumulation strategy, making any sale a potential signal that even the most committed holders reassess their positions. The company still holds over 840,000 Bitcoin, maintaining its position as a major holder, but the policy shift damaged market sentiment disproportionately to the actual volume sold.

Also Read: ETF outflows and macro fear put Bitcoin and Ethereum under pressure

The price decline triggered a cascade of forced selling via leveraged long liquidations, exceeding US$283 million within 24 hours and representing a staggering 1,520 per cent spike. This liquidation wave amplified the downward move, transforming what might have been an orderly correction into a more violent repricing. The sudden dip triggered over US$90 million in Bitcoin-linked futures liquidations as leveraged long positions were liquidated. High leverage left the market fragile, and when prices broke below the US$72,000 support level and the 50-day moving average, the technical structure shifted to a bearish bias. The liquidation cascade acted as a downward amplifier rather than a root cause, but its impact on market psychology proved significant.

Strategy’s own stock suffered more than Bitcoin itself, sliding between 4.5 per cent and 6.5 per cent as investors recalibrated the premium on the corporate treasury model. This suggests that markets question whether holding Bitcoin on corporate balance sheets remains an unalloyed good when the asset shows weakness. The divergence between Bitcoin’s struggles and traditional markets’ strength highlights a critical reality. Institutional capital currently favours assets with clear earnings growth and fundamental value creation over speculative stores of value.

The near-term outlook for Bitcoin remains bearish as long as it stays below US$73,000. If the cryptocurrency holds above US$71,000, consolidation becomes possible, but a break below this support level risks a drop toward US$68,000. The key metric to watch involves ETF flow trends. A return to net inflows would signal returning demand and could stabilise prices, but continued outflows suggest further downside risk.

Also Read: Southeast Asia should take note: Bitcoin mining is no longer an industrial game

This market divergence reflects broader macroeconomic currents. US factory activity expanded for a fifth consecutive month in May, providing fundamental support for equity valuations. Meanwhile, Bitcoin struggles without similar fundamental anchors, relying instead on sentiment and flow dynamics that have turned negative. The contrast between the superchip-driven rally of Nvidia and the liquidation spiral of Bitcoin encapsulates the current market preference for tangible innovation over monetary speculation.

Investors face a critical choice between participating in the AI-driven equity boom or betting on a crypto recovery that shows few immediate catalysts. The data suggests smart money currently favours the former, rotating capital toward assets demonstrating clear growth trajectories while reducing exposure to more speculative positions. Until Bitcoin can reclaim the US$73,000 level with conviction and ETF flows stabilise, the path of least resistance points lower, even as traditional markets continue their march to record highs.

The post Bitcoin down 3.32% as US$283M in liquidations wipe out leveraged traders: Saylor’s power? appeared first on e27.

The entire tech universe seems to be talking about OpenClaw right now. I am part of that conversation too, though perhaps from a slightly different angle. I am not talking about it because I am actively using it, but because I am acutely aware that I am not.

From AI models and chatbots to AI browsers, autonomous agents, and now the promise of 24/7 virtual assistants like OpenClaw, the pace of technological evolution feels relentless. If I am being honest, there are moments when I simply want to stop chasing. A recurring thought crosses my mind: perhaps it is wiser to wait until things stabilise, until there is a mature version ready for plug-and-play adoption rather than trying to learn while standing inside a technological tornado.

The fear of missing out still exists, of course. But over time, I have learned to pause and replace urgency with a quieter question: why?

So I did what any curious person does at 2 pm on a Thursday. I fell into a YouTube rabbit hole, searching for terms like “Claude Cowork,” “absolute beginner’s guide to Operator,” and “the only Agents 101 you need.” What struck me most was how measured the creators were in their language. No one was shouting from the rooftops, urging immediate adoption. Instead, they framed the value pragmatically: automation replaces repetitive work, and startups often need something like a tireless intern to handle operational tasks that consume time but create little strategic value.

One example stood out. A founder demonstrated an agent he built to monitor his financial dashboard daily. Rather than hiring a full-time CFO, the agent tracked expenses, organised cash-flow visibility, and alerted him when spending exceeded predefined thresholds. It was impressive, efficient, and undeniably useful.

Also Read: SEA founders are asking the wrong fundraising question

Watching that, I turned the lens back on myself. Do I actually perform repetitive work every day? Is there a part of my workflow that genuinely needs automation? If I had a reasonable budget, would I hire an intern to handle the tasks filling my hours? And if I am honest about where I want to grow, does that growth even require expanding a team?

Surprisingly, my answers were mostly no.

My work revolves around people. When I facilitate workshops or coaching sessions, I want to understand participants personally, to sense shifts in energy that cannot be captured in summaries or transcripts. When I write, the starting point is often a lived experience or an emotion that no agent can originate. When I explore partnerships, trust is built through warmth, nuance, and conversation long before efficiency becomes relevant.

This does not mean I reject technology. I already rely on tools for transcription, research, and editing support. These technologies enhance my work, but they do not replace a process that feels fundamentally human. I have yet to encounter a workflow so repetitive that I genuinely want to delegate it entirely to an autonomous agent.

One particularly popular OpenClaw demo showed a founder generating an entire website while relaxing at a beach club, presenting a vision of ultimate productivity freedom: work continues while life happens elsewhere. Yet that example left me wondering whether the goal should always be to let technology expand work into every corner of our lives.

Also Read: Hospitality needs to treat AI agents like a new channel, not a new feature

After the video ended, I found myself staring at the full moon outside my window. For a few minutes, there was no dashboard to optimise, no productivity system to refine, and no urgency demanding attention. Just quiet.

Perhaps the real risk today is not missing out on the latest tool. The greater risk is allowing every technological breakthrough to convince us that we must move faster, do more, and automate everything before we fully understand why we are doing it at all.

OpenClaw will continue evolving. Organisations will integrate agents to accelerate execution, reduce operational load, and unlock new forms of scale. That future feels inevitable. But adoption should not be driven purely by hype or fear.

Because what I hope most of us are not missing is something far less discussable than technology: the unremarkable, irreplaceable moments that never appear on any dashboard. The dinner with someone who matters. The unexpected view that catches you mid-scroll. The bowl of noodles that somehow tastes like a memory rather than a task between meetings.

The operator will still be here tomorrow. It will evolve, merge into larger systems, and find its place in workflows that truly need it. The more powerful question, however, is not can I use this? But what do I actually want more of in my life – and does this technology help me get there?

That, perhaps, is the real shift happening beneath all the noise. Technology is no longer only about capability. It is becoming a mirror, forcing us to decide not just how efficiently we work, but how intentionally we live.

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