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From tariffs to Powell’s speech: Will crypto dips and stocks rally?

The recovery in global risk sentiment, spurred by US President Donald Trump’s announcement of a 90-day pause on reciprocal tariffs (except for China), brought a much-needed sigh of relief to equity markets. Yet, beneath the surface, there’s a nagging sense that we’re not out of the woods. The bond market’s volatility, surging inflation expectations, and a weakening consumer sentiment all point to deeper uncertainties that could shape the trajectory of the global economy in the weeks and months ahead.

Let’s unpack this week’s developments and what they mean for investors, consumers, and policymakers.

The US equity markets staged an impressive rebound last week, with the Dow Jones Industrial Average climbing five per cent, the S&P 500 gaining 5.7 per cent, and the Nasdaq Composite surging 7.5 per cent. These gains came after a tumultuous period where markets were rattled by fears of an escalating trade war, particularly between the US and China. Trump’s decision to pause tariffs for 90 days on most trading partners, allowing time for negotiations, was a pivotal moment. It signaled a potential de-escalation, at least temporarily, and markets responded with enthusiasm. The CBOE Volatility Index (VIX), often called Wall Street’s “fear gauge,” reflected this shift, dropping to 37 after spiking above 50 earlier in the week. That’s still elevated compared to historical norms, suggesting investors remain on edge, but it’s a far cry from the panic levels seen during the height of the tariff uncertainty.

The bond market told a different story. The selloff in US Treasuries was striking, with the 10-year Treasury yield jumping nine basis points to 4.48 per cent and the two-year yield climbing 12 basis points to 3.97 per cent. This was the largest weekly surge in yields in over two decades, a clear signal that investors are bracing for higher inflation and possibly tighter monetary policy. The ongoing US-China trade war, despite the tariff pause for other nations, continues to stoke fears of supply chain disruptions and rising costs. When goods become more expensive due to tariffs, businesses often pass those costs onto consumers, fueling inflation. The bond market’s reaction suggests that investors are betting on this scenario playing out, even if equities are basking in the tariff reprieve for now.

Also Read: SEA fintech faces funding slump in Q1 2025, Singapore and crypto buck the trend

The US Dollar Index, which measures the greenback against a basket of major currencies, closed lower last week, adding another layer of complexity. A weaker dollar typically supports commodities priced in dollars, and we saw that dynamic play out with gold soaring past US$3,200 per ounce, a two per cent gain for the week. Gold’s rally wasn’t just about a softer dollar—it was also driven by recession fears and the safe-haven demand that kicks in when trade wars escalate. Similarly, Brent crude oil jumped 2.26 per cent to settle at US$65 per barrel, buoyed by comments from US Energy Secretary Chris Wright about potentially ending Iran’s oil exports to pressure the country over its nuclear programme. Geopolitical tensions, layered on top of trade uncertainties, are keeping energy markets volatile, and that’s something I’ll be watching closely in the weeks ahead.

On the economic data front, the picture is sobering. The University of Michigan’s preliminary consumer sentiment index for April plummeted 11 per cent to 50.8, its lowest level since June 2022. This sharp decline reflects growing anxiety among Americans about the economic fallout from tariffs, rising prices, and uncertainty about jobs and growth. Even more concerning is the surge in inflation expectations, with the one-year outlook hitting 6.7 per cent, the highest since 1981. That’s a staggering figure, and it underscores the psychological impact of the trade war rhetoric and policy shifts. When consumers expect prices to keep rising, they may pull back on spending or demand higher wages, both of which can create a feedback loop that drives inflation higher. For the Federal Reserve, this is a nightmare scenario—balancing growth, inflation, and now trade-driven disruptions.

Over the weekend, the Trump administration added a twist by exempting smartphones, computers, and other tech devices from reciprocal tariffs. This move was a relief for markets, particularly in Asia, where tech supply chains are heavily integrated. Asian equity indices traded higher in early sessions today, and US equity futures pointed to a positive open. The exemption makes sense from a consumer perspective—hitting tech products with tariffs would have driven up prices for everyday goods such as iPhones and laptops, risking a backlash. But it also highlights the delicate balancing act the administration is trying to perform: projecting strength on trade while avoiding self-inflicted economic wounds. I suspect this exemption is a pragmatic nod to the reality that tech is the backbone of modern economies, and disrupting it too severely could backfire.

Looking ahead, all eyes will be on Federal Reserve Chair Jerome Powell’s upcoming speech. Investors are desperate for clarity on how the Fed plans to navigate this inflationary environment, especially with consumer sentiment tanking and inflation expectations soaring. Powell has been cagey in recent comments, emphasising that the Fed is monitoring trade policies closely. If he signals a hawkish tilt—perhaps hinting at pausing rate cuts or even tightening policy to combat inflation—it could dampen the equity rally. Conversely, a dovish stance might boost stocks but risks fueling inflation further. It’s a tightrope walk, and Powell’s words will carry immense weight.

Also Read: Gold soars, stocks teeter, crypto seesaw: The world awaits Trump’s trade hammer

China’s first-quarter GDP and monthly activity data, due this week, will also be critical. The trade war with the US is undoubtedly weighing on China’s economy, and weaker-than-expected numbers could reignite fears of a global slowdown. Given that several markets will be closed for Good Friday, trading volumes may be thinner, potentially amplifying any market moves. My sense is that investors are in a wait-and-see mode, parsing every headline for clues about the direction of trade talks and monetary policy.

The cryptocurrency market, meanwhile, has been a mixed bag. Bitcoin slipped more than two per cent on Sunday, trading at US$83,482 during Asian hours. Ethereum fell below US$1,600, and altcoins showed varied performance. The crypto market’s sensitivity to trade policy signals is intriguing—when tariffs on Chinese electronics were floated, digital assets wobbled, likely because of fears that supply chain disruptions could hit mining hardware or broader tech sentiment. Yet, Bitcoin advocate Michael Saylor remains undeterred, using social media to double down on his “Buy the Future” mantra. His latest post, timed with Bitcoin’s brief rally to US$83,246, underscores his belief that cryptocurrencies are a hedge against economic chaos. I’m skeptical about Bitcoin’s role as a reliable safe haven—it’s still too volatile and sentiment-driven—but Saylor’s conviction is a reminder of the passionate community behind it.

Ethereum’s technical picture offers some hope for bulls. After finding support at US$1,449 last week, it’s hovering around US$1,638. A close above US$1,700 could spark a rally toward US$1,861, supported by a Relative Strength Index (RSI) that’s climbing toward neutral territory. But the risk of a drop to US$1,300 looms if support breaks. XRP, meanwhile, is showing resilience, stabilizing at US$2.14 after a 14.28 per cent recovery. A break above US$2.23 could push it toward US$2.50, though it needs to hold above its 200-day EMA to sustain momentum. These technical levels matter for traders, but the bigger driver for crypto will be macro developments—trade policies, Fed signals, and global growth.

As I reflect on this week, my view is one of cautious optimism tempered by realism. The tariff pause and tech exemptions are positive steps, but the underlying tensions—US-China trade frictions, inflation fears, and consumer unease—aren’t going away. Equities may continue to climb if trade talks show progress, but the bond market’s warning signs and weak consumer sentiment suggest fragility. Gold’s strength and crypto’s volatility reflect a market searching for anchors in uncertain times. For investors, diversification and vigilance are key. For policymakers, the challenge is to avoid tipping the economy into recession while addressing legitimate trade concerns.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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GXS Bank acquires Validus Capital to accelerate SME financing solutions

Singapore-based digital bank GXS Bank has announced the acquisition of Validus Capital, the Singaporean subsidiary of Validus Investment Holdings (Validus Group), following regulatory approval.

The all-cash transaction saw Validus Capital become a wholly owned subsidiary of GXS Bank.

The acquisition positions GXS Business Banking to cater to small and medium-sized enterprises (SMEs) across all stages of their development. The digital bank already provides services to single-ownership businesses through the GXS Biz Account, an operating account with daily interest and no fees, and the GXS FlexiLoan Biz, an unsecured line of credit.

Also Read: Validus secures up to US$50M from HSBC to support Indonesian MSMEs

The integration of Validus Capital, anticipated to be completed by the end of the year, will allow the bank to serve larger SMEs requiring solutions to optimise their cash flow and access timely financing via short-term trade finance and supply chain financing.

All existing loans disbursed by Validus Capital will continue to be serviced without disruption.

Muthukrishnan Ramaswami, Group CEO of GXS Bank, stated: “There are significant synergies from this acquisition. As a bank, we are leveraging our balance sheet strength, digital ecosystem, and regional network to enhance and extend Validus Capital’s digital lending solutions to more SMEs in the region.”

He further elaborated that Validus Capital’s suite of financing solutions will enable GXS Bank to assist SMEs in unlocking cash flows promptly. “Instead of waiting for payment for work done or goods supplied, SMEs will be able to take advantage of our supply chain finance and working capital loans powered by Validus Capital. The sooner they receive capital, the faster they can invest in growth opportunities,” he added.

Also Read: Validus, TTC Group, Do Ventures form JV to boost SME lending in Vietnam

Established in 2015, Validus Capital is one of Singapore’s leading digital lending platforms catering to SMEs. To date, it claims to have disbursed over US$1 billion in supply chain financing and working capital loans to thousands of SMEs in the region.

While the Singaporean business is now part of GXS Bank, Validus Group will maintain its headquarters in Singapore. It will intensify its focus on its core markets in Indonesia (operating under the brand Batumbu) and Thailand. Notably, Validus is now profitable at the group level, a significant achievement for a fintech in the region, with Batumbu having been profitable for the past three years.

Last September, Validus partnered with global banking giant HSBC to raise up to US$50 million in debt facility. The capital is being deployed through Validus’s Indonesian subsidiary, Batumbu, to support local MSMEs and address the country’s financing gap.

GXS Bank, holding a banking licence from the Monetary Authority of Singapore, is owned by a consortium comprising Grab Holdings and Singtel. It operates as part of a regional network of digital banks, working closely with GXBank in Malaysia and Superbank in Indonesia. Validus Group is recognised as a leading SME lending marketplace in Southeast Asia, having financed US$5 billion to SMEs across ASEAN.

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Moom Health raises US$2.7M to drive innovation in women’s wellness

[L-R] Moom Health co-founders Mili Kale and Maya Kale

Singapore-based women’s health supplement brand Moom Health has announced SGD3.5 (US$2.7) million pre-Series A funding round led by Wipro Consumer Care Ventures, the venture funding arm of Wipro Consumer Care & Lighting.

Existing investor DSG Consumer Partners, alongside Racer Ventures (the family office of the Eu family and founders of Eu Yan Sang) and HPRY Holdings (a private investment firm supported by Kuok Khoon Hong, the co-founder and Chairman of Wilmar International) participated.

Also Read: Empowering women in healthtech: The role of technology in driving inclusive workplaces

Moom Health will utilise the newly secured funds to invest in product innovation, bolster its supply chain and team, venture into new markets, and expand its digital and retail footprint.

Furthermore, Moom aims to set new benchmarks in product innovation, community engagement, and research and advocacy within the women’s health domain.

Founded in 2021, Moom Health creates natural remedies for modern Asian women in the form of expert-backed supplements. Whether someone is starting their gut health journey or looking for more natural ways to manage PMS symptoms, Moom’s products and educational resources are designed to provide the quality and clarity women have been looking for in the supplement aisle.

Moom has a presence in Singapore, Malaysia, and Hong Kong.

Also Read: The most-funded healthtech startups in Southeast Asia: A decade in review

“There is a need for products built by the Asian woman, for the Asian woman and it’s amazing to be at the forefront of this movement—especially in the women’s health sector! We have seen tremendous growth in products like Happy Hormones and Healthy V, hero SKUs targeting specific need states (Fertility/PCOS/Postpartum Recovery & Vaginal Health, respectively) of the modern Asian woman, and view that as a ‘wake up call’ that products like these are not only needed, but wanted in the market!,” said co-founder, Mili Kale.

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The product management strategy behind building AI agent platform

Automation is evolving, and AI agents represent the next big leap. However, building a product that truly resonates with users is still an unproven and challenging process.

In a recent episode of the Startup Project podcast, I had the opportunity to dive into this topic with Jacob Bank, Founder and CEO of Relay.App. Our discussion shed light on the intricate product development journey of an AI agent-building platform. Here are some key takeaways for product managers, engineers, and entrepreneurs looking to build AI-first automation products.

The path to product clarity and the power of iteration

Relay.App’s journey highlights the power of iteration and market feedback. The early days were a period of exploration. Founded in 2021 with the vision of enhancing cross-tool coordination using AI, the team experimented with multiple approaches—building eight or nine different prototypes—before landing on a viable product direction.

Initially, the idea was to use AI to bridge gaps between various tools as the number of SaaS tools in any given organisation have been increasing over the last decade. But the real breakthrough came when Relay App shifted its focus to capturing repeated tasks that blend automation with human judgment. This pivot led to the development of a workflow tool that sits between Zapier-style automation and Asana-style task management.

The shift to AI agents

Despite some early traction, the Relay App team recognised a fundamental issue: positioning themselves as a workflow automation tool limited their reach. The “no-code workflow automation” label resonated with a niche audience but failed to capture the broader potential of AI-driven productivity.

This realisation led to a strategic shift—from an AI-powered automation tool to an AI agent-building platform. More than just a rebranding exercise, this represented a fundamental change in product philosophy. Instead of simply connecting tools, Relay.App now provides a platform where users can create intelligent agents that proactively work on their behalf.

Also Read: 4 ways to eliminate pointless tasks from your daily work

Integrations: A core competency, not an afterthought

One of the key takeaways from our discussion was the critical importance of integrations. Unlike other companies, Relay is not considering integrations as an afterthought and are not interested in open sourcing the ability to build integrations for the platform. They believe each integration has to carefully crafted and designed by top-tier engineering talent. For AI agents to function effectively, they must seamlessly interact with the tools that businesses already use.

Relay.App currently supports around 120 native integrations and is working toward expanding this number to 300-500. The goal is to cover essential business categories like email, calendar, messaging, CRM, and marketing automation. They firmly believe the usefulness of AI agents is directly tied to their ability to integrate deeply with existing workflows.

Human-in-the-loop

As AI becomes a bigger part of workflows, maintaining human oversight is crucial. Jacob stressed the importance of a human-in-the-loop mechanism that allows users to review and provide feedback on an agent’s planned actions before execution.

This approach not only builds trust but also enables continuous learning. AI agents can refine their behaviour based on user input, ensuring they align with human intent. Additionally, when AI deviates, users must have the ability to intervene and correct course in real-time. Striking the right balance between delegation and human interaction is essential to making AI augmentation truly effective.

Product-led content and community-driven growth

Relay’s go-to-market strategy heavily revolves around product-led content. Jacob actively creates content—LinkedIn posts, YouTube tutorials—demonstrating real-world use cases for AI agents. This helps users understand the product’s capabilities and lowers the barrier to adoption.

Beyond content, fostering a community is a key part of Relay.App’s growth. By enabling users to create and share templates, the platform has built a cycle of organic adoption where users contribute back to the ecosystem, making the product more valuable for everyone.

Also Read: From silicon to sustainability: Data centres in a warming world

The future of product development: AI-powered teams

Jacob envisions a future where product teams are leaner, more agile, and empowered by AI. Instead of large teams with highly specialised roles, individuals will take on “player-coach” responsibilities—combining strategic vision with hands-on execution.

This shift is possible because AI agents can automate routine tasks, allowing human employees to focus on higher-level problem-solving. The key lies in identifying the right tasks for automation and designing workflows that integrate AI seamlessly with human expertise.

Key lessons and the road ahead

Jacob Bank’s journey with Relay.App offers valuable lessons for anyone building AI-first products:

  • Embrace iteration: Finding the right product-market fit requires constant experimentation.
  • Listen to customers: Market feedback is essential in refining product vision.
  • Prioritise integrations: AI agents must work seamlessly within existing tool ecosystems.
  • Maintain human oversight: A human-in-the-loop model builds trust and ensures better AI alignment.
  • Leverage product-led content: Educating users through compelling content drives organic growth.

As the agent landscape evolves, product teams need to remain flexible and adaptable. By focusing on tangible value, robust integrations, and user-centric design, companies can successfully navigate the challenges of AI adoption and build transformative products.

Relay.App’s experience serves as a reminder: the real opportunity in AI isn’t in the hype—it’s in creating practical, user-friendly solutions that empower people to work smarter and faster.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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Wired for life: How technology is rewriting the human experience

In the digital age, few forces have reshaped our daily behaviour, cognition, and relationships as significantly as technology. It touches everything—how we work, learn, communicate, fall in love, manage money, and even entertain ourselves. But beneath the convenience and innovation lies a deeper transformation: one that alters how we think, interact, and understand what it means to be human.

This is not just a story about screens and software. It’s a story about people—how we live now, how our parents lived before us, and what we might be losing in the pursuit of seamless efficiency. It’s a story of change so quiet, so constant, that we often fail to see its full impact. The longer we go without questioning, the more invisible it becomes. We live surrounded by code, yet we seldom consider how deeply it codes us in return.

Technology is no longer a tool. It’s an environment.

It’s easy to think of technology as something we use—a neutral set of tools designed to simplify life. But that view is increasingly outdated. Today, technology functions less like a toolkit and more like an environment: invisible, omnipresent, and unavoidable. It seeps into the mundane and the monumental, defining not just how we perform tasks but how we perceive the world.

We wake to smartphone alarms and fall asleep to streaming platforms. Between those hours, we navigate a landscape of emails, instant messages, video calls, AI suggestions, fintech transactions, and algorithm-driven content. The result is a life defined not by occasional tech use, but by near-total digital immersion.

Unlike the physical tools of the past—a hammer, a typewriter, a rotary phone—modern technology wraps itself around us, responding to our behaviours, learning our patterns, and shaping our preferences. Our digital footprint is not a byproduct of usage—it is the product. Our choices are constantly guided, predicted, and monetised.

This constant exposure doesn’t just shape our schedules. It shapes us—our habits, attention spans, priorities, and even our values. Our sense of time has warped. Our expectations have shifted. And because it feels normal, we rarely question what it’s doing to us. We live inside it—breathing it in, building our identities through it, one notification at a time.

Work: Productivity gains, boundary losses

In professional life, technology has delivered extraordinary efficiencies. Remote work tools like Zoom, Slack, and Notion have enabled collaboration across time zones. Cloud platforms allow simultaneous editing. Automation cuts down administrative labor. Real-time communication platforms make cross-functional alignment easier than ever. In many ways, we are doing more, faster, and from anywhere.

But these benefits come with trade-offs. The same platforms that allow flexibility also blur the lines between work and rest. Employees are now expected to be reachable beyond business hours. The culture of “instant reply” fosters anxiety, while back-to-back virtual meetings have eroded the value of deep, uninterrupted thinking. Burnout is no longer confined to the office—it follows us home, hiding inside our inboxes and notifications.

The psychological effect of being always connected, yet rarely present, has also transformed work culture. The illusion of productivity often replaces meaningful contribution. We reply faster but think slower. We multitask more but retain less. The workplace has become a place of constant communication, rather than considered creation.

Also Read: Transforming tech performance: A brain-friendly growth approach

Compare this to our parents’ generation, where work ended when you left the office. The boundaries were rigid—but they were also protective. Today, many of us can’t remember the last time we fully switched off. We scroll through emails on holidays, check messages before sleep, and tether our value to the pace of our responses. In trying to stay connected, we’ve lost connection—to ourselves, our rhythms, and sometimes, our purpose.

Entertainment and leisure: Infinite choice, Diminished patience

In entertainment, we’ve moved from scarcity to excess. Where previous generations waited for weekly television broadcasts or visited cinemas for communal experiences, today’s viewers binge-watch entire series in one sitting. Streaming services have created a culture of immediacy. If content doesn’t capture our attention in seconds, we skip.

Games have become immersive social ecosystems. Virtual concerts, in-game events, and expansive multiplayer worlds are now part of the entertainment fabric. Music is available instantly. News, curated by algorithms, is consumed through endless scrolls and five-second clips. Our leisure has become a high-speed buffet.

Yet, paradoxically, this abundance can dilute the quality of engagement. With every option at our fingertips, attention has become fractured. We consume more—but reflect less. The ability to sit with discomfort, ambiguity, or slow storytelling is rapidly diminishing. We swipe through lives, stories, and emotions with the same flick.

Patience—a virtue once practiced in the days of dial-up internet and rental DVDs—is becoming obsolete. Entertainment now competes not just for our interest, but for our attention span, which technology itself has shortened. The result is content that is optimised for immediacy, not depth. And in choosing the quick thrill, we risk losing the lasting memory.

Social interaction: Connection without closeness

Few areas have changed as dramatically as human relationships. Social media allows us to stay in touch with people across the globe. Messaging apps offer instant contact. Dating platforms broaden access to potential partners. In many ways, communication has never been more convenient.

But convenience is not the same as closeness. Relationships today are often mediated through screens, where nuance is lost, and misinterpretation is common. Digital communication flattens emotion, shortens conflict resolution, and favors immediacy over depth. We respond with emojis instead of emotion.

Even among friends and family, in-person interactions have been replaced by emojis, voice notes, and heart reactions. We witness each other’s lives through curated highlight reels, creating an illusion of intimacy while reducing the space for vulnerability.

Loneliness is rising—even in hyperconnected environments. A generation raised on digital interaction often struggles with in-person communication. Conflict resolution, empathy, and emotional literacy are learned less through human models and more through transactional texts. We know how to reply quickly, but not how to listen deeply.

For our parents, relationships required more effort—but perhaps, more presence. Letters, face-to-face talks, and long phone calls built slower, but sturdier bonds. Today’s always-on culture risks producing connections that are broad but shallow. We connect more often, but feel less seen.

The cognitive shift: Comparing generations

To fully appreciate the psychological impact of technology, it’s useful to compare generational experiences. Our parents lived in a slower world. They navigated cities without GPS, relied on memory rather than cloud storage, and maintained social bonds through in-person interaction or handwritten letters.

Also Read: Key insights for tech startups: 6 essential tips to thrive in the industry

Their challenges were logistical: how to reach people, how to access information, how to find opportunities. But these constraints strengthened certain mental faculties—spatial memory, emotional resilience, delayed gratification, and critical thinking. Tasks required planning and anticipation. Mistakes taught patience.

Today’s digital natives live in a different mental landscape. Answers are immediate. Directions are automated. Choices are abundant. While this fosters adaptability and digital fluency, it often comes at the cost of deeper cognitive engagement.

Memory is externalised to search engines. Boredom is eliminated by scrolling. Problem-solving is often bypassed by automation. We don’t need to remember, wait, or struggle. And yet, these very experiences—waiting, struggling, memorising—are the ones that develop resilience, creativity, and insight.

Brains adapt to the environment they’re in. In a fast-paced, notification-driven world, we are training our minds to be reactive rather than reflective. And reflection—not reaction—is where meaning lives.

Fintech, realtech, and the tech that touches everything

Beyond consumer tech, advancements in fields like fintech and realtech are also reshaping human behaviour. Fintech platforms have democratised access to banking and investment. Mobile wallets and peer-to-peer lending have made transactions faster and more inclusive, especially in emerging markets.

Fintech has enabled a generation of self-directed investors. Apps that simplify trading, budgeting, and savings gamify financial literacy. Yet, the same apps can foster addictive behavior. The stock market becomes a game. Cryptocurrencies become high-stakes bets. Financial decisions become more impulsive and less informed.

Realtech—technology that intersects with real estate—has also transformed how we live. Smart homes offer convenience but also raise privacy concerns. Virtual property viewings speed up transactions but reduce human engagement. AI-driven pricing tools prioritise efficiency over fairness. Homes are becoming data centres.

These technologies shape not just economic behaviour, but how we relate to space, risk, and ownership. They make complex decisions seem simpler, but they also remove opportunities for negotiation, personal judgment, and human nuance. The home is no longer just a shelter—it’s a system.

Also Read: Tech career switch: A woman’s guide to upskilling and advancement

Rethinking the role of tech: What are we really building?

As we move further into an AI-powered, hyper-connected future, the key question is no longer what technology can do.

It is: What should we let it do? And at what cost?

Some critical reflections worth asking:

  • Are we using technology to deepen our lives—or simply to distract from them?
  • What habits are we unconsciously developing, and what abilities might we be losing?
  • Are our relationships more frequent but less meaningful?
  • Has convenience replaced resilience?
  • What does “being human” mean when machines start thinking, recommending, and even feeling on our behalf?
  • Are we designing tools for empowerment—or surrendering agency to algorithms?

Technological progress is inevitable. But human progress is not. That depends on how thoughtfully we respond to the world we’re creating.

Because in the end, the future will not be built by tech alone. It will be built by us—our choices, our ethics, and our willingness to remain human in an increasingly automated world. It’s time to decide not just how we use technology, but how we let it shape our lives. The tools are powerful. The responsibility is ours.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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AI agents are the new workforce: How to implement them successfully

The age of digital employees is here — powered by cutting-edge Generative AI. From automating routine IT tasks to transforming customer service, AI Agents are rapidly reshaping how modern businesses operate.

Whether you’re in Finance, HR, IT, or Customer Success — the shift is clear: intelligent agents are now the new workforce accelerators.

From chaos to clarity: Start with strategy

Before diving into tools, align your AI journey with clear business outcomes.

Identify inefficiencies: repetitive IT helpdesk requests, manual invoice processing, slow lead qualification… sound familiar?
Set performance benchmarks: Improve Invoice Processing Time, reduce Customer Response Time, or boost Net Promoter Scores.
Use data-powered tools like Process Mining (Celonis, UiPath) and Analytics Dashboards (Power BI, Tableau) to identify the biggest wins.

Choose the right AI agents

Not all agents are created equal. Match your business needs with the right type of AI Agents:

  • Personal productivity agents: Think ChatGPT or Microsoft Copilot
  • Organisational agents: Use Slack AI or Notion AI for better internal efficiency
  • Business process agents: Automate heavy-lift processes like budget forecasting or loan reviews
  • Cross-org agents: Enable seamless data sharing and automated workflows

Also Read: AI agents redefine art: Unlocking boundless creative possibilities in a new digital era

Choose between:

  • Autonomous agents for hands-off automation
  • Copilot agents to augment your human teams

Implementation made easy

Start small, win big:

  • Focus on high-impact, easy-to-integrate agents
  • Prefer out-of-the-box platforms like Agent.ai or Microsoft Copilot Studio
  • Browse directories like G2, Capterra, and AI Agents Directory to discover what fits your need

🏁 Begin with a pilot → Test with one team

🔗 Integrate smartly → Connect AI with your existing systems (ERP, CRM, HRMS)

🔄 Refine continuously → Use KPI-driven feedback to improve performance

Empowering people, not replacing them

AI is here to help, not replace.

  • Communicate clearly: Highlight early wins
  • Up-skill teams: Host hands-on workshops and AI awareness sessions
  • Redefine roles: Shift teams from routine to strategic tasks. Let HR focus on people, not paperwork

🏅 Recognise AI Champions in your teams and incentivise adoption!

Scale with confidence

Once you’ve piloted successfully, it’s time to scale.

  • Expand to other departments and create an AI Center of Excellence (CoE)
  • Maintain strong AI governance: Monitor fairness and bias (use tools like IBM AI Fairness 360). Ensure data privacy compliance (GDPR, HIPAA, SOC2). Keep human oversight in critical decision-making

Establish regular feedback loops and A/B test new AI enhancements for continuous optimisation.

Your AI-powered future starts now

The future isn’t just coming — it’s already in motion. Whether you’re an early explorer or preparing your teams for next-gen automation, now is the time to embrace AI agents as business enablers.

This article was originally published on LinkedIn and is available here.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

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Work, tech, and talent: Kristen Lim on the evolving nature of leadership

e27 has been nurturing a supportive ecosystem for entrepreneurs since its inception. Our Contributor Programme offers a platform for sharing unique insights.

As part of our ‘Contributor Spotlight’ series, we shine a spotlight on an outstanding contributor and dive into the vastness of their knowledge and expertise.

In this episode, we feature Kristen Lim, Co-Founder and CEO of Rockrose, an executive search firm focused on top talent in Emerging Tech, Fintech, and Web3. With over a decade in headhunting, she has placed 120+ senior leaders across 30+ global firms and brings cross-cultural insight from working with corporates, startups, and remote teams across Western and Asian markets.

Thoughts, goals, and journey

Lim began her career in finance but made a deliberate shift into recruitment driven by a personal desire to understand and master sales. What started as a curiosity quickly became a long-term calling — one she has never looked back from. Today, she is the Co-Founder and CEO of Rockrose Executive Search, a firm she built to continue her mission of connecting exceptional talent with the right opportunities.

Passionate about both people and technology, Lim operates at the intersection of these two domains. She works closely with companies to identify and place leaders who will drive innovation and shape the future through technology. “My personal goal is to become a thought leader in leadership, recruitment, and entrepreneurship,” she said.

Lim sees GenAI as a major force transforming industries and is closely following its impact on talent, leadership, and the workplace. “The most recent trend is GenAI. It is evolving at lightning speed, and I believe the future of work will look very different from today,” she added.

The driving force

Lim has been an active contributor to our thought leadership community since joining the programme last year. Drawing from her experience in executive search and her deep interest in people and technology, her writing delves into themes such as the future of work, evolving trends in human resources, and navigating modern career paths.

“A friend told me about this programme, and I felt it was a great opportunity to share my insights with readers who are just as passionate about technology as I am,” she said.

Also Read: Kevin Shepherdson: Transforming data privacy and AI governance in ASEAN

Her advice to those beginning their thought leadership journey is simple: “Just start contributing — your future self will thank you for it. Writing helps shape our thoughts and ideas, and it helps us grow in ways we may not see immediately.”

Juggling too many things?

Work-life balance plays a critical role in entrepreneurship. Unlike traditional jobs, running a business often blurs the line between work and personal time, making it essential for founders to consciously create space for rest, reflection, and non-work pursuits. Maintaining this balance isn’t just about well-being—it directly impacts clarity of thought, resilience, and the ability to lead effectively over time.

Lim believes that work-life balance, especially for entrepreneurs, is less about maintaining strict boundaries and more about creating a fluid integration of personal and professional priorities. She sees entrepreneurship not as a means to escape work demands, but as a way to design a more intentional and flexible lifestyle that aligns with her values and energy.

She said, “Entrepreneurship allows me to balance work and personal life more effectively, and it becomes work-life integration rather than just work-life balance. My strategy for growth is to have an open mind and stay curious. If we seek to learn from others, the learnings will present themselves, we just need to keep an open mind and have the willingness to learn.”

Staying in the loop

Staying current in a fast-evolving industry like tech and recruitment requires more than just reading the news or attending events. For Lim, it’s about being in constant dialogue with the people driving those changes. She believes that staying connected with peers, clients, and leaders across sectors is one of the most effective ways to understand where things are headed and how to adapt.

“Through conversations with the leaders in my field. I talk to leaders and executives day in and day out. These conversations help me stay ahead of the curve on the latest developments in technology and how they impact workplaces and culture,” she said, explaining how she remains informed and relevant.

For those who want to stay informed, she recommends subscribing to Rockrose Pulse on Substack, a platform she curates to share observations on the job market and the evolving relationship between technology and people.

Take a look at her articles here for more information and perspectives on her expertise.

Are you ready to join a vibrant community of entrepreneurs and industry experts? Do you have insights, experiences, and knowledge to share?

Join the e27 Contributor Programme and become a valuable voice in our ecosystem.

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The AI-energy paradox: Will AI spark a green energy revolution or deepen the global energy crisis? — Part 3

For AI to drive a green revolution, innovations in computing efficiency and clean energy integration are crucial. This article explores next-gen AI hardware, energy-aware algorithms, and policy strategies that can align AI’s growth with sustainability.

Pathways to sustainable AI: Tech innovations and policy responses

For AI to truly spark a green revolution, innovation must focus on making computing more efficient and integrating AI growth with clean energy systems. This involves advances in hardware and software, as well as smart policies, to nudge the industry in the right direction.

Technological levers for efficient AI

  • Next-gen AI chips (ASICs and photonics)

Traditional CPU/GPU architectures are not very energy-efficient for AI workloads. Enter specialised AI accelerators. Companies like Lightmatter are developing photonic (light-based) chips that perform AI computations using photons instead of electrons, massively reducing energy loss as heat.

Lightmatter’s chip reportedly achieves 9 petaflops per watt of performance — orders of magnitude beyond conventional silicon. If such optical computing scales up, future AI models could run on a fraction of the energy today’s do. Similarly, Google’s TPUs and various startups’ AI ASICs are tuned for maximum throughput per watt, offering 2 to 5× improvements over general GPUs.

Inspired by the human brain, neuromorphic chips (like Intel’s Loihi 3) use networks of “spiking” neurons that are extremely low-power. They excel at tasks like pattern recognition with minimal energy. Intel reports up to 76 per cent lower energy for LLM inference with neuromorphic approaches on some workloads. While still experimental, these could allow AI systems that learn and operate continuously on tiny power budgets — think AI co-processors that sip power like a LED lightbulb.

  • Algorithmic efficiency (better software)

On the software side, there’s a push for efficient AI algorithms — for example, techniques like model pruning, quantisation, and knowledge distillation, which create smaller models that run faster. A pruned or distilled model can often achieve 90 per cent of the accuracy of a large model with, say, 50 per cent less computation required.

OpenAI and others are actively researching ways to maintain capability while cutting out the “waste” in neural networks. In training, new optimisation methods and architectures (like sparsely activated models) promise to reduce the compute needed to reach the same accuracy. These advances directly translate to energy saved.

  • Carbon-aware computing

Software is also helping schedule computing tasks at times and places where energy is clean. For instance, Microsoft Azure’s carbon-aware workload scheduling now shifts nearly 40 per cent of AI jobs to regions or times where renewable energy is abundant. If the wind is blowing in one data centre region, Azure will queue more AI jobs there, and pause or move jobs from another region that’s on fossil power at that moment. This kind of intelligent orchestration can significantly cut the effective carbon footprint of AI computations.

  • Energy-proportional computing and PUE improvements

Data centre engineers continue to drive down overhead so that almost every watt goes to computing, not waste. Average Power Usage Effectiveness (PUE) has improved (some hyper-scale centres are at a PUE of 1.1 or lower, meaning 90+ per cent of energy powers IT equipment).

Techniques like better airflow management, AI-controlled cooling (as discussed), and even waste heat reuse (heating nearby buildings with server heat) all contribute. The closer we get to a PUE of 1.0 and fully utilised servers, the more work (AI tasks) we can get done per unit of energy input.

Also Read: 5 reasons why energy management is key to individual and organisational success

Policy interventions

Governments can guide the AI-energy trajectory with targeted policies and standards:

  • Energy Efficiency Standards for AI Models

Just as there are fuel economy standards for cars, we may see efficiency standards for AI. The EU’s contemplated rule requiring 15 per cent energy efficiency improvement in new AI models is a first step. If major markets adopt similar rules or require reporting of AI energy use, it creates a competitive incentive to design greener AI. Transparency is key — imagine an “Energy Star” rating for AI services, where customers could choose a provider that is more energy-efficient.

  • Carbon-adjusted pricing and credits

Some regions are introducing tariffs or credits to encourage clean energy usage. For example, California and Bavaria (Germany) have floated the idea of carbon-adjusted power purchase agreements that penalise data centres drawing power from grids below a certain renewable percentage.

Under such schemes, if an AI facility isn’t using (or contracting for) at least 80 per cent clean power, it would pay a surcharge or face limits. This kind of policy pushes companies to invest in renewables or locate where clean power is available, to avoid financial penalties.

  • Dynamic electricity pricing

Grid operators like PJM in the US are implementing real-time pricing to manage peaks. PJM’s dynamic tariffs have encouraged data centres in its region (e.g., northern Virginia) to reduce peak load by 19 per cent — they respond to price spikes (often corresponding to dirty peaker plants coming online) by dialling down non-urgent workloads. Wider use of dynamic pricing will reward AI operations that can flex around grid conditions, effectively incentivising them to be more grid-friendly and efficient.

  • Accelerating clean energy permitting

One practical bottleneck for sustainable AI power is the slow permitting of new renewable projects and transmission lines. Policymakers can streamline this — for instance, the US Nuclear Regulatory Commission is fast-tracking approvals of advanced reactor designs aiming to have a set of SMRs approved by 2026, specifically with data centre use cases in mind.

Governments can also designate “energy corridors” for easier building of high-voltage lines to data centre regions, or provide grants for battery storage at data hubs. All these reduce the risk that AI’s growth outstrips green energy availability.

  • Support for R&D

Supporting the development of the above-mentioned technologies (optical computing, neuromorphic, etc.) through grants and public-private partnerships can speed their arrival. Given AI’s strategic importance, one can envision national programs to develop next-gen low-power AI hardware (the way there were initiatives for supercomputing in past decades). This not only helps the climate but also ensures a country’s AI industry remains globally competitive as efficiency becomes a differentiator.

The big picture is that a combination of technology innovation and forward-thinking policy can bend the trajectory of AI’s energy impact. It’s analogous to the auto industry — without better tech (EVs, hybrids) and policies (fuel standards, incentives), car emissions would have kept rising unabated. With them, it’s possible to have the benefits of mobility (or in our case, AI capabilities) while mitigating the harms.

For corporate leaders, staying ahead on these fronts means:

  •  Monitoring and adopting emerging efficient AI tech — perhaps experimenting with new accelerators or AI model optimisations that cut costs and footprint.
  • Engaging with policymakers or industry groups to help shape sensible standards (it’s better to help craft the rules than be caught off-guard by them).
  • Committing to transparency in AI energy use and emissions. Some leading companies already publish the PUE and carbon data of their data centres; extending this culture to AI operations builds trust and prepares the company for a future where stakeholders demand to know the climate impact of AI initiatives.

Next, we turn these insights into a concrete action plan for executives — what steps to take to ride the AI wave without capsising under energy costs or sustainability risks.

A tactical AI-energy strategy for corporate leaders

How can corporate decision-makers apply these insights in practice? Here we distill a practical guide — key questions to ask, and steps to take — to balance AI’s opportunities with energy and sustainability considerations.

Also Read: Why the future of space and energy storage might be growing in a Thai hemp farm

Five key questions every CEO should ask about AI and energy

  • How much energy do our AI operations consume? — Get a handle on the current state. Measure the power usage of your AI workloads (on-premise and in cloud). Understand the scale: is it five per cent of your IT energy use? 50 per cent? Quantify it in kWh and dollars, so you have a baseline. Also project how this might grow with planned projects (if you adopt a new AI tool, will it double your compute hours? Triple?). You can’t manage what you don’t measure.
  • Are we using the most energy-efficient AI models and infrastructure available? — Audit your AI stack. Are there opportunities to use smaller models, or algorithm optimisations like batching and quantisation to cut compute? Are you running on last-gen hardware out of habit, when new AI chips could do the job with 1/2 the energy? Push your tech teams and vendors to justify choices in terms of efficiency, not just accuracy or speed.
  • Are we leveraging AI to optimise our own energy use? — This flips the script: use AI as part of the solution. Could AI tools help reduce energy waste in your operations (factories, offices, supply chain)? For example, using AI for route optimisation in logistics to save fuel, or for energy management in buildings (as some have done to cut HVAC costs by 15–30 per cent). Ensure your sustainability and facilities teams are exploring AI solutions — the ROI can be significant, and it creates a positive offset for the energy your AI projects consume.
  • Are we investing in clean-energy-powered cloud services (or data centres)? — When choosing where to run AI workloads, factor in the energy source. Major cloud providers now offer regions or options powered by 100 per cent renewable energy — utilising those can drastically cut the carbon footprint. If you run your own data centre, consider power purchase agreements for renewables or even on-site solar. Essentially, align your digital infrastructure with your renewable energy procurement.
  • Are we prepared for potential AI energy regulations? — Scan the horizon for laws that might affect your AI deployments. For instance, if efficiency standards for AI or reporting requirements come in a year or two, do you have the data to comply? If carbon pricing rises, do you know which AI projects would become more expensive to run? Engaging with industry groups and regulators proactively can give you a voice and early insight. Internally, scenario-plan for a future where “green AI” might be mandated either by law or by customers/investors.

Asking these questions at the C-suite level ensures that AI initiatives are not happening in a silo, but are integrated with energy management and corporate strategy.

Practical steps for sustainable AI adoption

Conduct an AI energy audit

Much like financial auditing, do an energy audit for AI. Map out all AI-related compute (data centres, cloud usage, edge devices) and tally the power usage. Identify hotspots — e.g., a particular analytics cluster or training workflow that draws a lot of power. This audit gives you a clear picture of where to target efficiency efforts.

It might reveal, for example, that 20 per cent of your AI jobs account for 80 per cent of the energy — maybe heavy model training that could be scheduled during off-peak hours or moved to a more efficient cloud zone.

Optimise and right-size AI workloads

Use the findings to implement quick wins:

  • Model right-sizing: Where possible, replace giant models with smaller ones or use transfer learning to avoid training from scratch. If a 500-million parameter model can solve the problem, don’t use a 50-billion one. This can cut computation dramatically.
  • Lifecycle management: Not all AI tasks need to run at highest frequency. Determine which jobs are mission-critical vs. which can be throttled or delayed in high load times. Leverage cloud auto-scaling to shut down idle resources (many companies find servers running when not needed — a pure waste).
  • Use AI to tune AI: It’s meta, but you can apply AI to improve scheduling and resource allocation for your AI jobs (similar to how DeepMind’s system works for Google). This can maximise utilisation and reduce idle energy burn.

Leverage AI for broader energy management

As noted, deploy AI solutions in your operations to save energy and costs. For example:

  • Implement an AI-based energy management system in corporate offices or factories (many vendors offer these).
  • Use machine learning to analyse production line data for energy inefficiencies (maybe a certain machine uses more power than it should — predictive maintenance can fix that).
  • Optimise logistics and travel with AI to reduce fuel use. Every kilowatt-hour or gallon saved here helps offset the extra energy your data centres might consume. And they directly save money, improving the business case for AI investments.

Adopt hybrid computing strategies

Not all workloads must run in power-hungry central clouds. Consider a hybrid AI approach: run smaller, latency-sensitive tasks on energy-efficient edge devices (or on end-user devices), and reserve big cloud compute for the truly heavy tasks. By using edge AI (which has no network transit and can be highly optimised), you reduce total energy per inference.

Also explore techniques like model distillation to create lighter versions of cloud models that can run on-premises or on cheaper hardware when appropriate. This hybrid mindset ensures you’re not always using a sledgehammer (huge cloud instance) for a nail (simple task).

Also Read: On the precipice of energy transition

Prioritise green cloud providers and contracts

When negotiating with cloud or data centre vendors, make sustainability a key criterion. Ask providers about their PUE, their renewable energy percentage, and their roadmap for low-carbon operations. Some cloud providers now offer dashboards showing the carbon emissions of your cloud usage — use those insights.

If you operate your own facilities, sign renewable energy contracts (PPAs) to cover your AI electricity use with clean energy. Also, work with utilities on programs (many utilities have “green tariffs” or will help with renewable projects if you’re a large load). Align your procurement so that as your AI energy use grows, your renewable supply grows in step.

Collaborate with industry and policymakers

Given the broader grid challenges, it’s wise for companies running big AI workloads to have a seat at the table. Join industry consortia focused on sustainable data centres or AI ethics that include energy impact. Engage local governments if you’re building data facilities — perhaps partner on community solar/storage so the investment benefits both you and the grid.

Being proactive can also help shape favorable policies (for instance, incentives for using local clean power or faster permitting for your backup generators etc.). Don’t wait to be caught by surprise regulations; help shape the narrative that AI can be part of the climate solution.

Scenario planning and risk mitigation

Finally, include energy security in your risk assessments for AI. Ask “what if” questions: What if power is constrained in Region X — do we have failover in a different region? What if electricity prices spike 3× — does our AI project still make economic sense, and can we hedge that risk?

Have backup plans for critical AI services if rolling blackouts or energy rationing ever hit (not unthinkable in some grids). By planning for these contingencies, you ensure AI deployments are resilient and won’t be derailed by external energy shocks.

By taking these steps, executives can balance efficiency, cost, and sustainability in their AI adoption. The companies that follow this playbook will likely have a smoother ride scaling AI — with lower bills and stronger ESG credentials — than those who treat energy as an afterthought.

Conclusion: A contested energy future

AI’s rise presents both a monumental challenge and an opportunity for the energy landscape.

On one hand, AI’s energy demands are forcing a reckoning: power grids are under strain, carbon goals are at risk, and companies may face tough trade-offs or regulatory hurdles if they ignore the issue. On the other hand, AI offers unprecedented tools to drive efficiency, optimise energy systems, and accelerate the transition to cleaner power.

For corporate leaders, the takeaway is clear: the future will belong to those who integrate AI and energy strategy. The organisations that treat energy as a core element of their AI plans — investing in efficiency, securing sustainable power, innovating with AI in their operations — will lead the pack.

They’ll enjoy more reliable growth (because they won’t hit energy ceilings), better public trust, and likely cost advantages as well. Those that ignore the linkage may find themselves facing energy supply crises, skyrocketing costs, or regulatory roadblocks that stall their AI ambitions.

The choice isn’t whether to adopt AI — that wave is here and necessary to remain competitive. The choice is how to do so responsibly and strategically. Companies that can harness AI and champion sustainability will shape the narrative of the coming decades. They’ll prove that innovation and green objectives can reinforce each other, not collide.

In the end, will your company spark the AI energy revolution, or be caught flat-footed by it?

By asking the hard questions now and taking decisive action, you can ensure that AI becomes a driver of efficiency and positive change — a win-win for your business and the planet, rather than a zero-sum trade-off. The green energy revolution and the AI revolution can be two sides of the same coin, but it will take foresight and leadership to make that vision a reality.

Will your company shape the AI-energy future — or be shaped by it? The decisions made today will determine the answer. The opportunity is to lead boldly, invest wisely, and create an AI-powered future that is sustainable, secure, and full of possibility for generations to come.

Thanks for reading!

What do you think?

This is part three of a three-part series exploring AI’s energy impact. Read part one here and part two here.

This article was originally published here and co-authored by Xavier Greco, Founder and CEO of ENSSO.

Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.

Join us on InstagramFacebookX, and LinkedIn to stay connected.

Image courtesy: DALL-E

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Turbulence and tenacity: How SEA’s startups are turning trade wars into opportunity

As trade tensions between global superpowers continue to simmer, their ripple effects are being felt across industries and borders — though not always in predictable ways.

In Southeast Asia, startup founders and industry leaders are navigating this shifting landscape with a mix of caution, adaptability, and opportunism. From budget hospitality to fintech, AI-powered supply chains to legal and consulting services, many startups see the ongoing tariff and trade wars not just as a threat, but also as a catalyst for innovation and regional resilience.

Several businesses, particularly those without direct ties to US supply chains, remain largely insulated from immediate disruption. Yet, broader macroeconomic effects — like weakened consumer purchasing power, rising operational costs, and tighter access to capital — are prompting businesses to rethink strategy.

Also Read: Venture capital in a fragmented world: How trade wars are redrawing SEA’s startup map

We spoke with several startup founders and experts in the region to learn how the trade wars will impact their businesses and industries.

Below are what they said to us:

Amit Saberwal, founder and CEO of RedDoorz, a budget hotel network

Reddoorz is a domestic business operating in the hospitality industry and is more resilient to global shocks like tariffs. With no direct linkage to the US supply chain, we don’t see any immediate impact on our business.

If anything, we anticipate customers with reduced incomes will seek more affordable accommodation. This will help our business, as we have strong product offerings in this space.

Having said that, the industries impacted by the trade wars might lay off the workforce which will eventually reduce the purchasing power of the Indonesians.

These trade shocks might also trigger Private Equity players to take a back seat and give strategic buyers the advantage. This will again create an opportunity to accelerate our M&A activities with the support of our existing investors.

Kevin Lee, CEO (Indonesia and Malaysia) at GHL Systems/NTT Data Payment Services

For fintech firms, especially in the B2B payment space, there is still a good chance to promote their products to the US as payment is not a commodity at the moment and is still very much a tech product-driven business. When comes to payments, everyone is looking towards Southeast Asia to expand their businesses.

Izwan Zakaria, founder and Managing Partner of Izwan & Partners, a tech- and startup-focused law firm

Amid fluid tariff conflicts between the US and China, Southeast Asia is emerging as a strategic neutral zone, leveraging its balanced ties with both of these large economies. In the semiconductor sector, the region has been serving the industry as a supply chain diversification strategy in the downstream processes like assembly, testing, and packaging, attracting increased foreign investments into countries like Malaysia, Singapore, and Vietnam.

For the fintech industry, global startups use Singapore as a domicile entity, leveraging its robust ecosystem and regulatory framework, and expanding into the broader Southeast Asian region. Additionally, regional trade agreements like the Regional Comprehensive Economic Partnership (RCEP) foster a more business-friendly environment and enhance market access.

Complementing this, the Association of Southeast Asian Nations (ASEAN) via the ASEAN Financial Innovation Network (AFIN) promotes cross-border policy harmonisation, making the region an ideal launchpad for fintech startups.

Charles Wong, co-founder of Cinnex, an AI-powered supply chain automation company

The escalating US tariff war will fundamentally reshape global supply chains. We’ll likely see a shift from US-centric trade to stronger regional alliances across Asia-Pacific and EU markets, with manufacturing hubs diversifying globally to mitigate tariff risks.

Also Read: US-China trade war escalates: Markets and Bitcoin plummet

This challenging environment creates significant demand for AI-powered solutions. SMEs now face critical pressure to optimise procurement processes and supplier relationships where every cost savings matter. Our technology enables seamless buyer-supplier integration, dramatically accelerating quotation responses and allowing businesses to rapidly identify optimal suppliers while maintaining competitive pricing despite market disruptions.

Cameron Priest, CEO at Pahoia, which builds, acquires, and operates businesses

It’s too early [to draw any conclusion on the impacts of the trade war on the region]. I’m talking to many of our e-commerce clients at AMP (which acquires and builds world-class tools for e-commerce entrepreneurs). There are a lot of shocks, uncertainty, and big issues with cash positions. I’m feeling frustrated since they’d be already working on moving to Thailand, Vietnam, etc., but there’s a lot of lack of clarity.

Right now, short-term supply chain financing is a big issue for many businesses as they scramble to pay tariffs. In another business, we’ve been sourcing APIs from China and are now trying to expand to Germany and India efficiently.

Rajib Saha, co-founder and CEO of Indepay, an instant payment service provider

The recent shift in tariffs emerges as a positive dimension for homegrown deeptech startups such as Indepay. As increased costs force reduced dependency on global technology, businesses will need to rely on at-par local solutions.

Indepay was created in Indonesia, for Indonesia. Our deep tech capabilities allow us the agility to customise and build scalable solutions to meet these gaps.

The EU has already hinted at parting ways with US-based payment solutions and building local alternatives instead. This trend is likely to be seen across countries and companies alike.

Agile startups can leapfrog into large businesses through the right R&D, talent, and opportunity fulfilment. A focused approach, with government support, also boosts local skill development and retains the region’s best minds.

US companies such as Apple, Amazon, and the Silicon Valley giants are testaments to the fact that products that solve market needs attract better talent and industry partnerships.

Javier Ruiz Jimenez, founder and CEO of Awarala, a Malaysian boutique consulting firm

The real impact of the trade wars will be on entire industries moving back to their home countries, now that automation reduces the importance of labour costs (e.g., semiconductor testing and packaging).

If I had to choose an industry that can thrive almost anywhere, I’d pick software. It has no major barriers, it is super low cost, almost no hard dependencies that can be taken away, and can also give rise to adjacent industries, like specialized hardware.

Also Read: Trump tariffs shake markets: Why gold soars as Bitcoin stumbles in 2025

Successful software companies can become powerful and valuable in negotiations—just look at TikTok. They can choose to host their servers with hyperscalers owned by different countries. Many hyperscalers have heavily invested in cloud data centres in Malaysia and rely on these software firms for returns, so they may pressure their governments to ease regulations.

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AI disruption unveiled: Hidden opportunities for startup survival and success

In an AI-powered world, one principle remains unchanged: Execution matters more than the business model itself.

While technical expertise and innovative ideas are valuable, they alone do not secure investment. Investors prioritise founders who can execute—who can take an idea, bring it to market, scale effectively, and navigate inevitable challenges with resilience and strategy.

This is why execution-driven teams often succeed even when their business models aren’t fully refined.

✅ A strong team with a less-than-perfect model can still create significant investment value.

❌ On the other hand, even the most well-crafted business model will fail if the team lacks the ability to execute.

At its core, startup success isn’t about having the “perfect” plan—it’s about having the agility to execute, adapt, and scale in a constantly evolving market. In an era where AI is revolutionising industries at an unprecedented pace, the ability to implement and iterate will separate the true innovators from those who simply follow the trend.

AI is reshaping the startup landscape

AI is no longer a distant future—it is actively transforming the way startups operate. What once required a large team of executives—CTO, CBO, COO—can now be managed by a lean founding team leveraging AI-driven automation.

Today, startups can scale from prototype (MVP) to revenue-generating businesses with fewer resources than ever before. AI-powered tools are enabling:

👨‍💻 CTOs to handle a larger scope of coding tasks with fewer engineers.

📊 CEOs to manage marketing, pitch decks, product design, and proof-of-concept (PoC) validation independently.

This shift allows startups to operate in a leaner, more agile, and cost-efficient manner.

Beyond startups, businesses across industries are embracing AI-driven automation to streamline operations and reduce workforce dependency. In the U.S. and Europe, companies are rapidly moving away from manual workflows toward AI-powered efficiency strategies.

AI isn’t just an enhancement—it’s a fundamental transformation that is redefining how businesses scale, innovate, and compete.

Winning in the AI-driven market: Strategy over hype

As AI continues to reshape industries, startups that go beyond simply adding AI features to existing products are the ones capturing long-term value. Rather than treating AI as an enhancement, the most forward-thinking startups are redesigning their businesses with AI at the core.

This shift requires a fundamental rethinking of problem-solving strategies. Instead of asking, “How can we integrate AI into what we already do?”, successful startups are reframing the question:

“How can AI completely transform the way we operate and create value?”

Also Read: Meet the trailblazers: 7 female founders from SEA selected for EY’s Entrepreneurial Winning Women 2025

Even within the same industry, companies are leveraging AI to drive deep innovation and differentiation, rather than just small, incremental improvements. The key is to move beyond surface-level AI adoption and use it as a catalyst for reimagining business models, automating decision-making, and unlocking new market opportunities.

Ultimately, success in the AI-driven market isn’t about merely adopting AI—it’s about strategically redefining how AI can drive real impact and industry-wide transformation.

What investors are looking for

Investors have become increasingly discerning about AI startups and how they leverage the technology. Today, they distinguish between:

✅ AI-native startups → Companies that build their business model around AI from the start, using it as a fundamental component of their strategy and product.

⚠ AI-enhanced businesses → Companies that add AI to existing products but haven’t fundamentally changed their approach. These businesses often struggle to prove AI’s core value to their model.

Investors are no longer simply looking for startups that use AI—they are asking how AI creates real, scalable value.

For startups seeking funding, the focus should not be on just incorporating AI but on leveraging AI to drive fundamental innovation and competitive advantage.

The pitfalls of AI integration

Many startups attempt to retrofit AI into existing products in an effort to make their business seem more cutting-edge. However, without a strategic approach, this can backfire.

❌ Superficial AI adoption – Some industries have little natural synergy with AI, yet companies within them are adding AI features simply to position themselves as “AI startups.” Investors, however, quickly see through these shallow attempts.

❌ Forcing AI into the business model – Startups eager to follow AI trends sometimes integrate AI in ways that don’t enhance their core offering, leading to unnecessary complexity without meaningful value.

For AI to be a true differentiator, it must enhance both the business model and the product itself.
Simply adding AI for the sake of rebranding rarely translates into long-term success.

The fear of AI: Resistance or reluctance?

History has shown that people often resist change, especially when it threatens familiar ways of working. In 19th-century Britain, the Luddites famously opposed industrialisation, going so far as to destroy machinery they believed would replace their jobs.

Fast forward to today, and we see a similar unease surrounding AI. Many fear that AI will disrupt industries, eliminate jobs, and render certain skills obsolete. However, much of this fear doesn’t stem from an actual inability to adapt, but rather from a reluctance to engage with and learn about new technology.

Innovation has always driven progress, and AI is no exception. Throughout history, technological advancements have eliminated some jobs but created entirely new industries in their place. The real challenge is not AI itself, but how individuals and businesses choose to respond to its rapid evolution.

📌 A willingness to learn and adapt is more crucial than ever.
📌 Those who embrace innovation often gain a competitive edge, while those who resist risk being left behind.

Also Read: Philippine VC Kaya Founders backs AI, fintech, and B2B innovators in 2025

Human-machine communication: A critical skill in the AI era

As AI rapidly advances, the way humans interact with machines is evolving. Modern AI, powered by natural language processing (NLP), no longer requires complex coding skills to operate. Instead, users can communicate with AI using everyday language, making human-machine interaction more intuitive than ever.

This shift is not just about convenience—it’s about accessibility and efficiency. AI systems are becoming faster, smarter, and easier to use, and this trend will only accelerate. Those who can effectively interact with AI will gain a competitive advantage, while those who struggle to adapt may find themselves at a disadvantage.

The misconception that AI is too technical or difficult to use is becoming obsolete. While a deep understanding of AI models like Large Language Models (LLMs) is valuable, the reality is that AI tools are being designed for broader accessibility—even for those without technical expertise.

📌 Rather than viewing AI as a complex system, individuals and businesses should actively engage with it in practical ways.
📌 Even basic familiarity with AI can provide a significant competitive edge in an increasingly AI-driven world.

In an era where AI is no longer optional but essential, the ability to communicate effectively with machines is becoming just as important as traditional digital literacy. Those who embrace AI as a tool for problem-solving and productivity will be at the forefront of the next wave of innovation.

Special thanks to Genya Smagin, Senior Manager, AI Strategy & Partnerships at SK Telecom for his valuable contributions to this article.

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