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.

—

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

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

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post The crypto liquidation spiral threatening another extended leg down to US$2.0T appeared first on e27.

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.

The post AI readiness must reach real work, not just training rooms 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.
—-

The post From a small town in Spain, Magnific is quietly reshaping the creative economy appeared first on e27.

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.

—

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

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

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

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

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.

The post Singapore leads on security governance but struggles to enforce it, report finds 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.

The post Cool Japan backs JumpStart’s Series C as vending machines reshape Indonesian retail 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.

The post Why one person + AI is becoming a serious workforce model appeared first on e27.

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.