The global business landscape is currently undergoing a structural shift so profound it has been dubbed the AI Apocalypse. For decades, the global economy has been optimized for a world where human intelligence was the primary scarce resource. We are now witnessing the “unwind” of that premium. As machine intelligence becomes a competent and rapidly improving substitute for human cognition across a growing range of tasks, the financial systems built upon billable human hours are undergoing a painful, disorderly repricing.

Defining the AI apocalypse

The term “AI Apocalypse” does not refer to a cinematic doomsday scenario of rogue machines. Instead, it describes an economic “left tail risk” where the rapid adoption of autonomous, agentic AI triggers a mass displacement of white-collar work. According to the viral Citrini Research report, “The 2028 Global Intelligence Crisis,” we are entering a period where the traditional value of human-led data synthesis, strategic insight, and process management is being eroded by systems that can perform these functions faster and at a fraction of the cost.

The source of market panic

The current anxiety among investors and professionals stems from the realization that AI is moving beyond simple “copilot” assistance to “agentic” autonomy.

• Agentic AI can execute complex workflows without human intervention.
• This shifts the paradigm from technology augmenting humans to technology replacing the need for human intermediaries.
• The fear is not just about job losses, but a deflationary spiral where the collapse of labor costs leads to a contraction in consumer spending and a fundamental devaluation of service-oriented business models.

High-profile casualties: why Accenture and IBM?

Legacy consulting and IT services giants like Accenture and IBM have found themselves in a unique and uncomfortable position. Historically, these firms thrived on “information asymmetry,” they possessed expertise their clients lacked.

1. Model Conflict: Their revenue models are heavily reliant on billable hours. If AI can produce a 5,000-word strategic white paper in minutes, a task that previously took a team of consultants weeks, the core value proposition of the “billable head” collapses.
2. Cannibalization: To remain relevant, these firms are selling the very AI tools that allow clients to bypass human consultants. They are essentially building their own replacements.
3. Exposure: With massive global workforces, Accenture alone employs over 740,000 people, they carry enormous fixed cost bases that become liabilities if utilization rates drop due to AI-driven efficiency.

Also read: Why Singapore manufacturers must embrace MES for the future

Evidence of the “intelligence crisis” in 2026

Recent developments suggest the scenario described by Citrini Research is already in motion. In early 2026, the market response to new enterprise AI tools from providers like Anthropic saw Accenture stock drop significantly, reflecting an “AI scare trade.”

The “re-pricing” is visible in the divergent narratives between corporate messaging and market valuation:

• Falling Bookings: In late 2025 and early 2026, reports emerged of declining quarterly new bookings for major IT services firms. For instance, Accenture noted a slowdown in its U.S. federal business, with internal sources describing a scramble for work-breakdown structure (WBS) coverage as employees fight for billable projects.
• Booking-to-Revenue Lag: While firms report high “Advanced AI bookings,” there is a noticeable lag in converting these into actual revenue. In December 2025, it was noted that while AI bookings nearly doubled, they represented only a small fraction of total revenue, suggesting that “AI pilots” are not yet replacing the massive revenue streams lost from traditional consulting.
• The End of Transparency: In a telling move during the Q1 fiscal 2026 earnings call, Accenture leadership announced they would stop reporting specific metrics for advanced AI revenue and bookings. The company argued AI is now “pervasive,” but critics view this as a way to mask the potential “cannibalization” where AI projects fail to offset the decline in legacy services.

Strategic vision for the Singapore business community

For businesses in Singapore, the AI Apocalypse presents a critical crossroad. The city-state’s high-value, service-led economy is particularly exposed to the “intelligence unwind,” but also uniquely positioned to lead the transition.

• From Intermediary to Architect: Singaporean firms must move away from being “implementers” of technology to becoming architects of AI-integrated ecosystems.
• Outcome-Based Models: Local businesses should accelerate the shift toward “outcome-based” or “fixed-price” pricing. Relying on billable hours in 2026 is a strategy for obsolescence.
• Sovereign AI and Ethics: As global firms struggle with workforce friction and legacy models, Singaporean enterprises can gain a competitive edge by focusing on “Sovereign AI” and robust AI governance, areas where human oversight remains a high-value, non-negotiable premium.

The AI Apocalypse is not the end of business, but the end of business as we knew it. The firms that survive will be those that embrace their own transformation before the market decides they are no longer necessary.

Also read: Top 5 best ERP software for building material business in Singapore | 2026 guide

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Conversations about equity in the digital economy often begin with representation. We measure the number of women who are founders, the number who work in engineering roles, and the number who hold leadership positions.

These numbers matter. But in practice, the inequalities many founders encounter begin much earlier, often before funding or hiring even enters the picture.

They begin with access to ecosystems.

Before a startup raises capital or gains media attention, there is a quieter question that shapes opportunity: who already understands how the ecosystem works.

The invisible infrastructure of opportunity

Technology ecosystems are built on networks.

Investors frequently meet founders through referrals. Speaking invitations often come through professional networks. Media coverage can begin with relationships that provide context and credibility.

For founders already embedded in these circles, the process can feel natural. For others, especially those entering the startup world for the first time, the pathways are far less obvious.

When I first started building companies, many aspects of the startup ecosystem were unfamiliar to me. Concepts like investor networks, founder communities, and speaking platforms were things I discovered over time rather than systems I was immediately part of.

This experience is not uncommon. Many founders know how to build products or market services, but the broader ecosystem around startups — capital networks, media exposure, and industry platforms — is something they encounter only after they begin building their companies.

Without that awareness, it is difficult even to know where opportunity exists.

Ecosystems compound opportunity

Being part of a network does not guarantee success. However, it can significantly increase the number of opportunities a founder encounters.

Visibility often creates a chain reaction.

A founder who gains exposure may receive speaking invitations. Speaking opportunities can build credibility. Credibility often leads to introductions. Introductions may eventually lead to partnerships or funding conversations.

Each step increases the likelihood of the next.

For founders who begin outside these networks, the challenge is different. They are not only building a company; they are also learning how the ecosystem itself operates.

That learning curve can be steep, particularly in industries where relationships and reputation play a significant role in opening doors.

Also Read: Cybersecurity and trust: A digital dawn for women in rural India 

Partnerships and hiring reflect similar dynamics

The same pattern appears in partnerships and hiring.

Startups frequently seek partnerships that allow them to expand their reach or credibility. Larger organisations often prefer partnering with companies that already demonstrate traction or visibility.

This creates a natural filtering effect. Companies with existing exposure tend to attract more partnership opportunities.

Hiring decisions can follow a similar logic. Many professionals prefer the stability of established companies with clearer career pathways. Startups, by contrast, rely on individuals who are comfortable with uncertainty and risk.

Neither of these patterns is inherently unfair. They are rational decisions from the perspective of individuals and organisations.

However, when combined, they can reinforce ecosystems in which opportunity tends to circulate among those already connected.

The role of AI in expanding reach

Artificial intelligence is often discussed as a tool that could level the playing field for founders. In practice, its impact is more nuanced.

AI primarily amplifies capability.

For founders who already understand how to conduct outreach, build networks, or create content, AI can significantly increase scale. Tasks that previously required teams can now be automated or accelerated.

Outreach campaigns, research, content creation, and operational workflows can be executed far more efficiently.

However, AI does not automatically replace strategic understanding. If someone does not yet know how to approach investors, position themselves publicly, or build professional networks, AI cannot fully bridge that gap.

In many ways, AI functions similarly to a team. It can execute instructions and scale processes, but the direction still comes from the founder.

For those who understand how ecosystems operate, AI can expand its reach. For those still learning, the underlying challenge remains the same: understanding how to navigate the system.

Also Read: Bridging the gender gap in GenAI learning: Strategies to get more women involved

Equity is also about ecosystem transparency

Discussions about equity in tech frequently focus solely on representation. Representation is important, but a wider set of factors influences ecosystems.

Geography, cultural context, and professional exposure all shape how easily someone navigates the startup environment.

In regions with mature startup ecosystems, founders may encounter investors, accelerators, and industry platforms early in their journey. In other regions, these pathways may be less visible or accessible.

Equity, therefore, is not only about who participates in the digital economy. It is also about how transparent and accessible the ecosystem itself is to newcomers.

Lowering the barriers to entry

One of the most meaningful ways to build a more equitable tech ecosystem is by making these pathways clearer.

This can include initiatives such as:

• Sharing knowledge about how investor networks operate
• Creating platforms where emerging founders can gain visibility
• Expanding mentorship and peer networks for early-stage founders
• Making ecosystem knowledge easier to access for those entering the industry

These changes do not eliminate competition or guarantee outcomes. Instead, they reduce the gap between founders who grow up inside startup ecosystems and those who enter them later.

Opportunity should not depend solely on proximity to the right circles.

Building more inclusive digital economies

The digital economy continues to evolve rapidly. Tools such as AI are lowering operational barriers and enabling smaller teams to build and scale companies more efficiently than before.

Yet the flow of opportunity within technology ecosystems is still heavily influenced by networks and access.

As ecosystems expand, the challenge is not only to increase participation but also to make the knowledge, relationships, and pathways that shape opportunity more visible.

When founders understand how these systems work, they can participate more fully and contribute back to the communities that support them.

Stronger ecosystems are not built only through innovation. They are built by ensuring that more people understand how to enter and navigate the opportunities they create.

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One of the challenges leaders often come up against is how they can best instil a performance culture within their group. Much of this effort is around instilling a vision and an accessible plan that your team will buy into, linked to the values and behaviours that will forge your culture.

However, no leader can build this alone. You will need support, and the best way to instil culture is to get to a self-policing environment where behaviours form a culture leading to results.

Not only are you looking to align and build, but you are also looking for the pillars of your new culture and values, those people who will, in turn, be your leaders and who will embrace the new journey together with you. It is these people who will live the behaviours that will underpin and create your culture.

Finding these heroes early is critical to building your team. By exemplifying the values and behaviours that will shape your culture, both you and your team will steer the mission forward. Do not be shy to tell these critical players that you are conferring on them this leadership role, and bring them into decision-making and people decisions where appropriate.

What this often requires, and particularly if the group is large or geographically distributed, is the identification and enrolment of your leaders of tomorrow, who will become the cultural beacons for the transformation you are embarking upon. It is they who will help to distill your communicated message, and who within your cohort will support, motivate, and focus everyone on the commonly aligned goals you have set.

This is easily stated, but some leaders can struggle to inspire a colleague to take on more responsibility, or to take a step toward acting more like a leader.

Here what not to do when encouraging folks to step up and take a more enhanced role:

Don’t pressure or force them

The first thing you must not do is to assume that they want it, especially if it the ask is not going to come with more compensation, or even an elevated title, straight away. Approach the topic, therefore, with some delicacy. The way you approach this can significantly impact their willingness and readiness to take on the new challenge.

Applying pressure can be damaging and counterproductive to what you are trying to build. Pressure can lead to resentment and stress, especially if you are asking someone to go beyond their comfort zone.

If a colleague is feeling pressured into a role that they are not ready or interested in, then their performance and morale can suffer. This can lead to avoidance, distraction, and possibly even a desire to leave the company, or to refuse to take on new challenges in the future.

You should as a leader be in the business of constructing a binding message and vision for the business. This should involve the lived values and behaviours you would like to see as part of your outreach to your colleague. Having an open conversation about their role, should be a motivational and supportive process, not one driven by pressure or coercion.

Also Read: 6 leadership lessons I learned after we raised our seed round

Instead, share your values and vision for the business and encourage your colleague by highlighting their strengths and potential. Be clear on the roles and behaviors that you are asking them to deliver but make it clear that the decision is theirs to make. Offer to provide support and resources if required to help them to succeed, should they choose to step up. Always allow them time to consider.

Do not overlook their current workload

Encouraging someone to take on more responsibility without considering their current workload can be overwhelming and counterproductive. Of course, it could be that you are asking someone to be a bridge to other functions, building the network and spreading the word about your area’s vision and focuses.

It could also be that you want them to deliver through their ‘soft skills,’ such as showing up with values and behaviours such as “better together,” which implies the forging of individual internal networks, and focus on collaboration to find solutions to problems in the business. Whilst you might not consider these actions impactful on a colleague’s current responsibilities, you must also consider how much this is pushing someone beyond their comfort zone.

This stress alone, if unsupported, could serve to impact their current effectiveness within their current role. Instead, have an open and clear discussion about their current role, workload, and your defined new set of responsibilities, and how these might be integrated. Consider also, redistributing some of their tasks, or providing additional resources if required. This shows that you care about their well-being and are also committed to their success.

Don’t ignore their career goals

Again, don’t assume that they want it. Assuming everyone wants to take on more responsibility or a leadership role can be a mistake. Not everyone has the same career aspirations. This could especially be the case if the new responsibilities do not align with their career goals. If this is the case, they may feel unmotivated or disconnected from their work, and this could lead to a lack of engagement.

Instead, discuss their career aspirations and how taking on additional responsibilities will align with their long-term career goals. Demonstrate how the new responsibilities will help them to grow and advance in their desired direction. If you treat the approach as you would a sale, and tailor yourself to their individual goals you can make the proposition more appealing and relevant.

Don’t fail to provide proper training and support, including coaching

Do not assume that your colleague can immediately handle the new responsibilities without some form of training and perhaps close mentorship and support, particularly if the ask is to be a pillar of support toward a change of culture through demonstration of soft skills, values and behaviours.

Without proper preparation, they may struggle with their new responsibilities, leading to frustration and possible failure. This can lead to discouragement and serve only to undermine confidence.

Also Read: 10 unarguable things that great leaders do

Instead, offer them all the support and advice, coaching and mentorship that they need. Regular check-ins can help address any issues early on and ensure that your colleague feels that they are invested in, and gaining all the support they need to be successful. Remember to always create and support a psychological safe space for your people to express themselves in, without fear or judgement.

Don’t neglect to recognise and celebrate their efforts

Encouraging someone to take on more responsibility without recognition and celebration of their successes, can lead to a lack of motivation and sense of appreciation. The worst outcome could be that the ask made of them can seem insincere or exploitative.

Failing to acknowledge hard work and achievements can come across as inauthentic and lead to feelings of being undervalued and under-appreciated. This of course can have the total opposite affect from creating a centre of leadership aligned to your vision, goals, values and behaviours.

Instead, you might create a pocket of counter programming in your organisational set up. Instead, regularly acknowledge their progress as positive reinforcement boosts morale, motivating them to forge ahead.

Public recognition is also a powerful motivator and especially useful when building culture, if aligned to your published values and behaviours, such as “Winning it together,” “Creating magic,” and so on. Do not miss the opportunity to reinforce what you are trying to build by not acknowledging your organisational cultural building heroes.

Conclusion

Inspiring a colleague to step up and take on more responsibilities in a leadership role, requires a thoughtful approach matched to authentic support.

By avoiding the pitfalls of pressuring them, overloading their workload, ignoring their career goals, failing to provide support and training, neglecting to celebrate them – you can create a positive and motivating environment to encourage growth, not only of this individual but of the team and culture you are building.

Ultimately, by identifying the leaders in your group and working closely with them, you can get to a self-policing environment. That said, you must keep it fresh and ensure that you are always working to bind the group to the goals you are setting. This is why it is vital to identify and map your talent and potential leaders in the group early.

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The post How not to elevate a colleague to a leadership role appeared first on e27.

In this interview, e27 speaks with Paruey Anadirekkul, Founder & CEO of Spacely AI, a generative AI platform focused on spatial design. As AI continues expanding beyond text and image generation into specialised professional domains, Spacely AI explores how automation can support architects, interior designers, and real estate professionals in visualising ideas faster and making earlier, more confident decisions.

This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and governing AI in real-world settings.

Generative AI for spatial design workflows

e27: Briefly describe what your organization does, and where AI plays a meaningful role in your work or offering.

Paruey: Spacely AI is a generative AI platform built for spatial design — helping professionals turn ideas, floor plans, and rough concepts into realistic 3D visualizations and design iterations in minutes. Our customers are primarily interior designers, architects, and real estate professionals, with a large portion based in the US and Europe.

AI plays a central role in both our product and internal operations. On the product side, we use proprietary generative models and 3D algorithms to automate rendering, layout variations, lighting adjustments, and even 2D-to-3D model conversion. This shortens early-stage design cycles dramatically, where most cost and timeline decisions are made.

Internally, AI supports product development, customer success, marketing experimentation, and data analysis. We treat AI not as a feature add-on, but as infrastructure — embedded into workflows to reduce repetitive tasks and allow our team and customers to focus on higher-value creative and strategic decisions.

Reducing friction in design and property decisions

e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?

Paruey: One clear way AI creates value is by reducing friction at the decision stage.

For real estate brokers and agencies use Spacely AI to help buyers see the true potential of a property. Instead of relying on imagination when viewing an empty or outdated space, buyers can instantly visualize renovations or new layouts. This reduces hesitation, shortens the purchase decision cycle, and increases conversion because clients can see what they are buying — not just what exists today.

For Interior Design Company, the value is in faster client alignment. Early in a project, most delays come from back-and-forth discussions about style, mood, and direction. By generating multiple realistic design options quickly, Spacely AI helps clients react to something concrete. This shortens alignment time, reduces revisions, and allows the team to move into detailed design work faster.

In both cases, the outcome is not just speed. It is clearer communication, stronger confidence in decisions, and better commercial results.

Also read: AI Pulse Exclusive: How CoBALT is designing AI that teams can actually trust

Balancing model performance and economics

e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?

Paruey: One key trade-off we faced was between model performance and economic sustainability.

Early on, we realized that generic models did not perform well for our target users. Interior designers and architects prompt very differently from casual users. So we invested time in fine-tuning and optimizing models specifically for spatial design workflows. That improved output quality and consistency, but it also increased compute costs.

At the same time, we had to balance cost, speed, and quality for different user tasks. High-fidelity rendering requires more GPU resources, while quick concept iterations can run on lighter infrastructure. Choosing when to use which model became a product decision, not just a technical one.

Gross margin is critical in AI SaaS. We had to design our pricing and token model carefully to protect margins while still delivering meaningful value to customers. The lesson was that AI capability alone is not enough — the real challenge is building a system where performance, user experience, and unit economics all work together sustainably.

Adoption momentum and integration challenges

e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?

Paruey: What worked better than expected was adoption speed. Once designers saw that AI could generate realistic concepts in minutes, not days, the willingness to experiment was high. Many professionals who were initially skeptical became regular users after seeing practical results in client meetings.

What proved more challenging was workflow integration. AI can produce impressive outputs, but using it effectively requires learning how to prompt well, iterate, and interpret results. There is still a skill curve. The value comes not from one-click magic, but from knowing how to guide the system.

Another challenge is the pace of AI improvement. Our rendering accuracy today is significantly better than it was six months ago. Costs, speed, and quality have improved rapidly. However, user perception often lags behind. Some customers still remember early limitations — like distorted elements — even though those issues have been resolved. Managing expectations in a fast-moving technology landscape has been just as important as improving the models themselves.

Rethinking workflows for AI adoption

e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate?

Paruey: One lesson leaders often underestimate is that AI adoption is not a plug-and-play purchase.

Buying access to an AI tool does not automatically produce better outcomes. The real value only appears when teams rethink their workflows around it. Many organizations try to layer AI on top of existing processes without changing how work is structured. That usually leads to frustration or underuse.

In our experience, the biggest gains come when teams revisit where time is spent, where decisions are delayed, and where iteration cycles are slow. Then AI can be embedded intentionally into those pressure points. Adoption requires training, experimentation, and sometimes redefining roles — not just software procurement. AI is most powerful when paired with operational redesign, not treated as a shortcut.

Also read: AI Pulse Exclusive: How Asia AI Association is advancing human-centred AI across the region

Starting with outcomes, not models

e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?

Paruey: One practical recommendation is: don’t start with the technology.

AI models change every few months — in cost, speed, and capability. If you start by choosing a model, you risk building around something that may soon be obsolete. Instead, start with outcomes. Define what you want to improve, map the current workflow, and identify the specific bottlenecks or repetitive tasks that can be automated or augmented.

Only after that should you evaluate which AI model fits the job today. Treat AI as a modular component. Design your architecture and processes so you can swap models as the landscape evolves. The advantage doesn’t come from picking the “best” model — it comes from building flexible workflows that can continuously improve as AI advances.

AI becoming invisible infrastructure

e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?

Paruey: Over the next 12 months, AI will become less visible and more expected.

In our industry, AI will shift from being a standalone tool to something embedded directly inside design workflows. Users won’t “use AI” as a separate step — it will be integrated into rendering, planning, cost estimation, and collaboration processes. The focus will move from generating impressive outputs to improving measurable outcomes like faster alignment, reduced rework, and higher win rates.

We also expect leadership expectations to mature. Instead of being impressed by what AI can generate, leaders will ask: did this reduce costs, shorten timelines, or increase revenue? The conversation will move from capability to accountability. AI will become operational infrastructure — evaluated on business impact, not novelty.

Adaptability over technical advantage

e27: Anything else you want to share with the audience?

Paruey: One final thought: AI will not reward the most technical companies — it will reward the most adaptive ones.

The advantage won’t come from having access to the latest model, because everyone does. It will come from how quickly teams experiment, measure impact, and adjust workflows. The organizations that win will treat AI as an ongoing capability, not a one-time transformation project.

Also, we should be honest: AI is not perfect. It makes mistakes. It requires oversight. But so do humans. The real opportunity is designing systems where human judgment and AI speed complement each other. When that balance is right, the results are not just faster — they are better decisions made earlier, where they matter most.

Also read: AI Pulse Exclusive: How Explico is building AI teachers can actually rely on

Designing AI for practical creative workflows

This conversation highlights how AI is increasingly moving into specialised professional domains beyond general content generation. In areas like spatial design, real estate, and architecture, the emphasis is shifting toward faster iteration, clearer decision-making, and integrating AI directly into everyday workflows. As adoption matures, organisations may find that the real advantage lies less in the models themselves and more in how effectively teams adapt processes to work alongside AI.

For more interviews, analysis, and real-world perspectives on how organisations across the region are applying AI in practice, click here.

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The post AI Pulse Exclusive: How Spacely AI is bringing generative AI into spatial design workflows appeared first on e27.

In the high-pressure world of startups, every decision counts. You are competing with others, trying to ensure rapid growth, and trying to beat your own pace for innovation. You feel the pressure to build a squad that can match your speed and work ethic.

While working towards meeting all these demands, a vital consideration is constantly ignored: empathy as an element of the hiring process. It is quite easy to assume that soft skills, such as empathy, are secondary to speed and technical know-how.

Empathy isn’t merely an afterthought to consider. It underlines strategic advantages. Leaders who lead with empathy foster trust, improve communication, and create more agile and responsive teams.

Integrating empathy into the hiring process does not entail an act of kindness. Rather, it involves fostering a culture that boosts a company’s appeal and helps retain sought-after employees.

Redefining empathy in hiring: More than just soft skills

Empathetic hiring is understanding candidates as people, not only as a CV or resume. It is listening, communicating, and ensuring courtesy and respect at every step of the recruitment process.

Absence of empathy in hiring can be very expensive. Negative candidate experience is a primary reason for high turnover rates and toxic work cultures. As an example, in their survey, KPMG found that 88 per cent of the business leaders admitted that they retained toxic employees to a damaging degree, costing companies anywhere from £2,000 to £10,000 per month.

During a major reorganisation, Avon focused on empathetic recruiting by clarifying their hiring processes and by improving job descriptions and feedback processes. The fairness of the hiring process dramatically increased through empathy.

Empathy widens soft skills assessment and improves retention, thus strengthening the company culture. While valuing empathy strengthens the investment in the team, it also improves the resilience and cohesion of the team.

Challenges of building people-first teams under pressure

As a startup founder, the need to scale is ever-present. This pressure can lead to difficult choices that force you to make quick compromises on your team’s cohesion.

Meeting your growth targets with a fixed number of employees creates a backlog of positions that must be filled immediately, however, a rushed recruitment process is guaranteed to yield bad results. The cost of hiring the wrong person for a position (role) at this pace will exceed what you would spend on a slowed recruitment process.

The temptation to hire based on personal values and backgrounds is ever-looming. However, this limits innovation and new ideas. Homogeneous teams are outperformed by diverse teams.

Also Read: Leading during uncertain times: The rising importance of empathy

Accessing wider segments of talent pools as a result of remote work systems comes with its challenges. For instance, some spontaneous interactions that promote collaboration are often muted as well. Synchronising in-office presence during hybrid working models can increase spontaneous collaboration while retaining the benefits of remote work.

Taking care of potential candidates while managing an organisation’s hiring needs is challenging, but empathy should play a role in the entire process. Neglecting empathy ultimately results in attrition of the talent pool as the culture becomes maladaptive.

How AI is revolutionising empathetic hiring in startups

The integration of AI tools can uncover a bare minimum threshold of unconscious bias in the evaluation of resumes and applications. This evaluation will focus on the skills and qualifications, ensuring no bias during evaluation on all fronts.

Estimation of the written and spoken sentiment with tone and cultural fit is done through NLP Technology in real-time during conversation. This makes it easier to think more profoundly, making it more holistic.

AI chatbots enable candidates to communicate instantly 24/7. They can provide answers and updates regarding the progress of the hiring process. This enhances the experience of candidates as they appreciate being continuously informed.

Hiring remains personal when there is a blend of AI accuracy with human scrutiny. The more tech solutions are used, the more devoid of empathy the outcomes become, resulting in senseless automation and poor judgment. Empathy–guided AI makes interactions intelligent and multifaceted.

Onboarding with empathy: The secret sauce to retention

Structures not designed with the new hire in mind can lead to rapid employee resignation. An alarming 20 per cent of new employees resign within the first 45 days of employment.

Onboarding with empathy solves this, so the aim is to respond to the issue through clearly defined dedicated learning pathways, continuous feedback loops, and providing psychological safety. Feeling valued is extremely important, especially from day one.

Contemporary applications of AI make it easier to manage adaptive onboarding by tailoring experiences to the user’s preferences and their approaches to learning. These systems will adjust to the new employee’s preferred pacing, provide real-time help as needed, and monitor participation rates to ensure that every employee receives the attention necessary for engagement.

Implementing empathy into the onboarding processes has both strategic effectiveness and intent. The organisations can benefit from increased retention rates, while the new employees set themselves up for ongoing success events by having AI customisation of their onboarding experience.

Building and managing people-first teams in high-pressure settings

Demonstrating empathy becomes vital in critical situations. It is the only path forward that everyone can agree upon. Trust, burnout, and performance are intertwined in coherent ways that empathetic leadership addresses and resolves.

Also Read: AI revolution: Balancing human empathy and robotic efficiency in customer service

Empathy-based leadership strategies:

• Active listening: Acknowledge and address concerns raised by team members. Listening strengthens trust, and trust fortifies communication.
• Transparent communication: Provide pertinent business intelligence like objectives, goals, and challenges to employees. Employees are granted autonomy from the shackles of ambiguity. Alignment with the organisation is further enhanced.
• Flexibility: Respond to the specific demands of the employees. A properly directed flexible work schedule enhances balance and leads to greater job satisfaction.

Authentic obstacles: Burnout and disengagement in scaling teams

Particularly in scaling up, startup centres face the challenge of burnout and disengagement, with disengagement standing out as a pronounced attribute. Struggles remain for the founders even as startups flourish. A survey indicated 53 per cent of founders reported burnout within the previous year, highlighting a need for sustainable growth.

The notion of burnout is a menace that stems from the offshoot threat facing organisations. Tasks are perceived to be augmenting solely to incentivise the employees to perform overtime work. That is where AI can truly shine.

The use of AI solutions solves all of these problems. MeBeBot is an example of a bot that can analyse conversations and retain the morale of the team as long as they are typing. This enables you to solve problems before they become too big to fix. Furthermore, AI Workload on Asana can also pinpoint an imbalance where a team member is taking on too much work.

The future is people-first and AI-smart

To fully develop resilient, high-performing teams, organisations’ AI-focused hiring won’t work; they will need to incorporate empathy-driven hiring processes. Enhanced candidate experience starts with integrating human connection to hiring and team management processes, which leads to stronger, connected, engaged teams in organisations.

Empathy allows leaders to establish this intelligent coexistence that intertwines the benefits of AI with those of human efforts. The effectiveness of AI simplifies and automates tasks like administration whilst reducing workplace biases, but trust, growth, and workplace culture are built around the human touch. Compassionate leadership guarantees that technology is not meant to take over the work that makes success achievable.

This is the perfect moment to reflect on your hiring approaches, as well as the structuring and managing of your teams. Adopt empathy-centred AI tools as they allow you to improve human interaction and communication within your organisation. This approach allows you to acquire amazing talent and, in addition, builds a company where people flourish and provide sustainable, impactful, positive contributions.

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What is an AI visibility tool?

An AI Visibility Tool is software that monitors and analyzes how a brand, product, or service appears on Generative AI platforms and Large Language Models (LLMs). Unlike traditional SEO tools, these tools focus on “generative engine optimization.” They track if a brand is mentioned or recommended by AI agents like ChatGPT, Perplexity, Gemini, and DeepSeek. Businesses can use this information to understand their digital footprint in the era of conversational search. They can also adjust their content strategies to remain visible to users interacting with AI.

Major challenges of SEO marketing in Singapore amidst the AI shift

In Singapore, buyers are increasingly relying on AI assistants instead of search bars. This shift presents a major challenge for the SEO marketing industry. The primary difficulty is the loss of “click-through” data. When an AI provides a direct answer, the user often does not visit the original website, which makes traditional traffic metrics less useful. In addition, Singapore is a multilingual hub. AI models often synthesize information differently across languages. This means a brand might be visible in an English search but invisible when a user searches in Mandarin or Malay. Navigating this requires a complete change in how marketers define “visibility.”

Also read: AI agents and ERP: Why Singapore businesses must act now

Why Singapore’s SEO marketing sector is dissatisfied with legacy SEO tools

The SEO marketing sector in Singapore is increasingly frustrated with traditional platforms. These tools were built for a search environment that is quickly changing.

• Keyword Focus: Traditional tools focus on keywords, but they do not account for how AI interprets context and intent.
• Lack of LLM Coverage: Most traditional tools do not track mentions within ChatGPT, Perplexity, or DeepSeek. This leaves marketers unable to see where recommendations are happening.
• Delayed Data: Traditional SEO data relies on periodic crawls. AI models and “live” search features require real-time monitoring.
• Inaccurate Localization: Legacy platforms often struggle to simulate the localized experience of a user in Singapore, especially when switching between different regional AI configurations.

Top 5 reliable AI visibility tools

Several tools have emerged to help marketers bridge the gap between traditional SEO and AI visibility. Here are the top five options currently available for the Singapore market.

1. BuildSOM

Pros

• The only global AI Visibility Tool that offers native non-English AI visibility monitoring, providing accurate results for Chinese, Malay, and other regional languages.
• Cost of effective prompt is among the lowest in the market, making it accessible for high-frequency tracking.
• Results from LLMs are captured through the browser UI, simulating the true consumer journey for realistic data.
• BuildSOM provides the largest coverage of LLMs among competitors, ensuring a comprehensive view of the AI landscape.
• A global tool that natively caters to DeepSeek, a critical LLM platform for the non-English community.

Cons

• Focuses on AI visibility, so its traditional SEO offering is limited.
• The user interface is English-only.

2. SEMrush

Pros

• Extensive historical database for keyword research and backlink analysis.
• Comprehensive site auditing tools to maintain technical health.
• Integrates social media monitoring with traditional search metrics.

Cons

• Support for non-English prompts is minimal. Results for non-English queries are often executed on English platforms, leading to inaccurate data for the Singapore market.
• The domain-based pricing scheme is expensive, with subscription fees easily increasing for brands managing multiple domains.
• LLM support is limited and does not include DeepSeek, Google AI Overview, or Copilot.

Also read: Why traditional SEO is dying in Singapore — and how AISEO pioneers are winning the next Blue Ocean

3. Ahrefs

Pros

• Backlink crawler and data accuracy for traditional link-building metrics.
• Detailed competitor analysis features.
• Content Explorer tool helps identify high-performing topics.

Cons

• The interface is designed for technical SEO professionals.
• The platform remains SEO-focused, with AI visibility playing a small role.
• Lacks native non-English AI visibility results.

4. Profound

Pros

• Focuses on brand mentions within generative AI responses.
• Offers data visualization tools to track brand sentiment.
• Provides alerts when brand citations change within AI models.

Cons

• The cost per prompt is higher than competitors.
• The Starter plan is restrictive, supporting only one LLM, forcing users into a more expensive plan.
• Does not support DeepSeek or Google AI Mode.

5. Otterly.ai

Pros

• Provides a view of brand mentions across AI chatbots.
• Includes automated reporting features for agency-client communication.
• Allows tracking of specific competitor sets.

Cons

• No free account option.
• The user experience is hampered by slow page loads and a fragmented dashboard.
• LLM support is restricted and lacks integration with DeepSeek and Google AI Mode.

Also read: The architect’s mandate: Building a resilient foundation for the intelligent enterprise

Preparing for the future

Many industry experts believe the transition to AI will be complete by 2028. At that point, traditional search may become secondary to AI-driven information gathering. It is recommended to try different AI Visibility Tools to find your best fit. Act quickly to integrate these tools, or prepare to become irrelevant in a world where AI agents are the new information sources.

Why we write this article

PRbyAI aims to share updated market news using our team’s tech knowledge, helping B2B customers make informed decisions.

About PRbyAI

PRbyAI is a tech-driven Martech startup leveraging cutting-edge AISEO to help customers generate leads and tap into new markets.

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Most digital products do not fail because of bad features. They fail the moment a user hesitates before clicking “confirm”.

That hesitation rarely comes from design alone. It comes from trust. Can I trust this payment to go through? Will my data be safe? If something goes wrong, will the system catch it?

In Southeast Asia, where digital adoption has scaled faster than regulation and infrastructure in many markets, that moment of hesitation matters more than ever. Cybersecurity is no longer a backend function. It is the layer that allows the digital economy to function at all.

Without it, fintech cannot move money, startups cannot scale across borders and users simply stop engaging.

Trust is the real product

Southeast Asia’s digital economy is projected to exceed US$300 billion in gross merchandise value, according to the e-Conomy SEA report by Google, Temasek and Bain & Company. Much of this growth is driven by payments, e-commerce and platform-based services.

But underneath that growth is a quieter system at work.

Every successful transaction depends on invisible checks. Fraud detection models flag anomalies in milliseconds. Encryption protocols secure user data. Identity verification systems confirm that the person clicking “pay” is who they claim to be.

Users do not see these systems. But they feel them.

A payment that processes smoothly builds confidence. A suspicious delay, an unexpected OTP loop, or a failed transaction does the opposite. Trust is not built through marketing. It is built through consistent system behaviour.

This is why cybersecurity is better understood not as protection, but as permission. It gives users the confidence to act.

Where systems break, trust follows

Despite this, many companies still treat cybersecurity as a compliance layer rather than a core part of product design.

This gap becomes visible when systems scale.

In fintech, fraud incidents continue to rise across the region. In Singapore, scam losses crossed a record SG$1.1 billion (US$814 million) in 2024, a 70 per cent increase year-on-year, with over 51,000 reported cases. The trend has continued into 2025. Victims lost SG$456.4 million (US$338 million) in just the first half of the year, despite stronger controls and enforcement.

Across Southeast Asia, the scale is even more significant. An estimated US$23.6 billion was lost to scams in the past year, with nearly one in ten consumers falling victim and 84 per cent expressing concern that scams are increasing. In Malaysia, fraud remains heavily driven by social engineering and impersonation schemes, with telecom-related scams alone accounting for RM715 million (US$150 million) in losses across nearly 29,000 cases in 2025.

These are not only security failures. They are trust failures.

When users lose money or data, they do not distinguish between a phishing attack and a platform vulnerability. The perception is simple: the system was not safe enough.

Startups often underestimate this. Early-stage teams prioritise speed, onboarding and growth metrics. Security is handled reactively, usually after an incident or when enterprise clients demand it.

This creates a pattern. Systems appear to work at a small scale, but begin to strain as transaction volumes grow, integrations multiply and cross-border complexity increases.

Also Read: Cybersecurity is not an IT problem: It is a trust architecture crisis

The overlooked risks inside organisations

External threats often get the most attention, but many vulnerabilities sit inside organisations.

Access control is a common blind spot. As teams grow across markets, permissions are rarely updated with the same discipline as product features. Employees retain access they no longer need. Third-party vendors are integrated quickly but not always audited thoroughly.

In HR systems, for example, inconsistent data structures and fragmented access can lead to deeper issues than simple inefficiencies. Leadership dashboards become unreliable. Headcount visibility weakens. Decision-making slows because the data cannot be fully trusted.

This is not typically framed as a cybersecurity issue. But it is part of the same trust layer. If internal systems cannot guarantee data integrity, external trust becomes harder to maintain.

AI and data amplify the stakes

The rapid adoption of AI tools across Southeast Asia is adding a new dimension to the trust equation.

Companies are integrating AI into customer service, analytics and internal workflows. In many cases, this involves feeding large volumes of data into third-party platforms.

The risk is not always immediate breaches. It is the gradual erosion of control.

Sensitive business data, customer information or proprietary models can be exposed through poorly governed usage. Employees may not fully understand what data is being shared or stored.

This creates a new kind of vulnerability. Not one driven by malicious attacks, but by unclear boundaries.

As AI becomes more embedded in operations, cybersecurity needs to extend beyond infrastructure into usage behaviour. Policies, training and system design all become part of the trust layer.

Moving from protection to trust design

The shift that companies need to make is not simply adding more security tools. It is rethinking how systems are designed.

Reactive protection focuses on preventing breaches. Trust design focuses on enabling confidence.

This can take simple but meaningful forms.

Clear transaction flows reduce uncertainty during payments. Visible verification steps reassure users without adding unnecessary friction. Transparent data policies help users understand how their information is used.

Internally, structured access controls and consistent data governance improve reliability. Systems become easier to audit, scale and integrate across markets.

The difference is subtle but important. Security stops being a barrier and becomes part of the user experience.

Also Read: Cybersecurity: The evolution from digital safeguard to economic governance

A regional challenge, not just a technical one

Southeast Asia adds another layer of complexity.

The region is not a single market. It is a collection of regulatory environments, infrastructure maturity levels and user behaviours. What works in Singapore may not translate directly to Indonesia, Vietnam or the Philippines.

This makes cybersecurity less about standardisation and more about localisation.

Payment habits differ. Identity systems vary. Regulatory requirements evolve at different speeds. Companies expanding across the region need to account for these differences while maintaining consistent trust standards.

This is where many rollouts struggle. Systems are designed centrally but implemented unevenly. Security controls become inconsistent. Gaps emerge between markets.

Users may not articulate these gaps clearly, but they feel them in the form of friction, delays or uncertainty.

Trust is the limiting factor

The next phase of Southeast Asia’s digital growth will not be limited by demand. Adoption is already strong. Digital behaviours are deeply embedded.

The limiting factor will be how much trust systems can sustain.

Cybersecurity, in this context, is not a defensive function. It is an enabling layer. It determines whether users complete transactions, whether businesses scale across borders and whether investors view systems as reliable.

For founders and operators, the question is no longer whether security is important. It is whether their systems are designed to be trusted.

Because in the end, users do not engage with infrastructure. They engage with outcomes.

And trust is what makes those outcomes possible.

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Small and mid-sized enterprises (SMEs) across Southeast Asia are entering one of their toughest hiring cycles in recent years. LinkedIn’s latest research suggests the talent crunch is becoming structural rather than cyclical. Nearly three in four Singapore-based SMEs say it has become harder to find qualified talent compared to last year.

The challenge goes beyond a shortage of candidates. Businesses are facing a widening skills mismatch, intensified competition for in-demand capabilities, and a surge of AI-generated job applications that add noise to hiring pipelines and increase screening workloads.

Also Read: How SMEs can become learning organisations, without the corporate bureaucracy

At the same time, AI is reshaping what companies look for in talent: from technical expertise to broader AI literacy.

In the first part of this interview, Elsie Ng, Director of Talent Solutions for Singapore and Malaysia at LinkedIn, shares insights on how SMEs can adapt to the evolving talent landscape.

Edited excerpts:

Singapore’s SME talent challenges are increasingly seen as “structural”, per LinkedIn data showing 71 per cent of respondents reporting greater hiring difficulty than last year. Beyond global trends, what regional factors may be contributing to more persistent talent constraints for startups?

LinkedIn data shows 71 per cent of hirers in small businesses say it’s harder to find qualified talent, yet 58 per cent of professionals report actively job hunting. This tells us that the labour market is still moving, i.e. people are looking and businesses are hiring, but the alignment isn’t landing.

To understand why, we need to look at the broader context. We’re seeing the labour market rotate toward a new era of work. In Singapore, hiring has slowed to about 20 per cent below pre-pandemic levels, shaped largely by economic uncertainty and monetary policy shifts. But there are pockets of opportunities, driven by AI.

We’re entering what I’d call a “new collar” era of work, one where the workforce increasingly blends knowledge work, advanced technical skills, and distinctly human strengths. AI is at the centre of this shift.

In Singapore, AI engineering roles now make up 4.2 per cent of all job postings on LinkedIn, up 40 per cent year-on-year, while AI engineering talent represents just 1.5 per cent of our member base and is growing at only 10 per cent annually. Demand is outstripping supply by a significant margin.

But it’s not just about engineering. Demand for AI literacy skills has surged over 70 per cent year-on-year and is now spreading into traditionally non-technical roles like marketing. AI is becoming a baseline expectation across the organisation, not just within technical teams.

As AI literacy becomes table stakes, human capabilities are gaining even more prominence. In Singapore, soft skills like communication, teamwork, leadership, and problem-solving are among the top 10 in-demand skills.

We also know that small businesses are growing and still hiring, albeit at a slower pace. SMEs grew 4.97 per cent in company numbers and 3.56 per cent in headcount year-on-year in October 2025, outpacing large enterprises.

And while hiring overall is down, large enterprises are driving the decline more sharply — down 42 per cent compared to 26 per cent for small businesses. The real challenge is that small businesses are hiring into a fundamentally different labour market.

Also Read: Talents remain an issue in AI proliferation, but here are 6 steps that businesses can do to tackle it

In this new era of work, skills matter more than titles, and many traditional hiring approaches haven’t kept pace with how quickly that’s changing.

For Singapore specifically, a few regional dynamics are adding to the structural challenge. Singapore’s position as a regional tech hub, combined with strong government support for AI adoption, creates significant momentum and opportunity, but also intensifies competition for in-demand skills.

Competition for in-demand skills tops SME pain points at 44 per cent. Which specific tech and AI roles/skills are SMEs in Singapore, struggling the most to fill?

The most acute gaps for small businesses lie in AI engineering and AI literacy skills.

Today, 7.7 per cent of employees in SMEs have AI engineering skills, compared to 20 per cent in large enterprises. Put simply, small businesses are operating at roughly one-third of the AI capacity of larger companies, which limits their ability to build, deploy, and scale AI solutions internally.

The gap is even wider for AI literacy, the foundational ability to understand and work effectively with AI tools. Over the past year, AI literacy in SMEs has grown five times slower than in larger enterprises. As AI spreads across industries and roles, this gap risks compounding over time, with real implications for competitiveness, productivity, and long-term resilience. If left unaddressed, the growing gap will widen existing inequalities in access to technology and opportunity.

But there’s a critical counterpoint: employees in small businesses are highly motivated to learn. Nearly half (49 per cent) are learning AI with employer-provided guidance or training. What’s more telling is the initiative they’re taking independently: 67 per cent are learning on their own time using free resources, and 53 per cent are paying for courses themselves.

When it comes to how they prefer to learn, the pattern is clear: employees want practical, hands-on experience. The top three preferences are learning through real-life projects and assignments (35 per cent), using AI tools to practice real scenarios (34 per cent), and virtual training and tutorials (34 per cent). The demand for upskilling is there. The challenge for SMEs is creating the structure and opportunity to channel that motivation effectively.

35 per cent of SMEs cite a sheer lack of qualified applicants. What factors tend to draw candidate attention toward larger employers, and how does LinkedIn help SMEs and startups in Singapore and Malaysia improve visibility and reach candidates with the right skills?

Many candidates are drawn to larger employers because of structured learning opportunities, especially as AI reshapes roles and expectations. In an era of rapidly evolving skills, access to upskilling has become a key deciding factor.

The data is clear: professionals want support from management when navigating AI. Two-thirds (67 per cent) of employees at small businesses in Singapore believe access to lifelong learning resources would boost their confidence in adapting to AI changes, and 66 per cent are actively looking for helpful content (resources, tools, and courses) to learn AI better. More than half (55 per cent) want leadership support to navigate AI-related changes at work. And critically, 65 per cent believe they can successfully reskill in AI regardless of age, with the right support.

Also Read: How startups can overcome the AI talent death

This is where small businesses can compete. While they may not have the scale of large enterprises, small businesses can offer something equally valuable: direct access to hands-on learning, clearer pathways to applying new skills, and leadership that’s closer to the work.

AI-generated applications now plague 40 per cent of SME hiring pipelines, bloating workloads. How is this “noise” disproportionately hammering resource-strapped startups versus larger firms, and what’s the real cost in time and missed hires?

In Singapore, 40 per cent of recruiters say they feel pressure to hire faster, while the same proportion say uncovering hidden-gem candidates is a top priority. For small businesses with limited hiring resources, higher application volumes quickly turn into longer screening hours and slower decisions. Reducing noise and surfacing a genuine fit early can make the difference between moving forward with confidence and missing out on the right hire.

AI-powered tools like Hiring Pro are designed to bring more clarity to that process. Rather than relying heavily on keyword matches or credentials alone, it evaluates candidates against the actual skills and criteria a business sets, using real-time data to surface stronger-fit shortlists.

For small teams, having that kind of support, almost like a hiring partner that’s embedded in the workflow, helps shift time away from manual filtering and toward meaningful conversations with the right people.

With AI adoption exploding, why should SMEs bet on “talent resilience” through tools when upskilling their existing teams might be cheaper and faster than chasing unicorns in a tight market?

It’s not either-or; upskilling and talent resilience need to work in tandem.

Upskilling is essential. In fact, small business employees are already showing strong initiative — learning AI through on-the-job guidance and training, while also investing their own time and money to stay relevant.

What we’re seeing, however, is a widening gap between how quickly AI capabilities are advancing and how slowly organisational systems and workflows are adapting and evolving around them. In that environment, training alone doesn’t always translate to real impact.

About 43 per cent of small business employees say they feel overwhelmed integrating AI into their work, and more than half (53 per cent) feel they’re not using it to its fullest capability. That tells us the challenge isn’t just access to courses; it’s how AI needs to be embedded into day-to-day roles.

Talent resilience means redesigning how capability is built and deployed at the organisational level:

• Embedding AI into everyday workflows, not treating it as a side project
• Segmenting capability — deciding what to buy, what to build, and what to raise literacy on
• Rotating employees through AI-enabled projects to build judgment and domain expertise over time

Upskilling keeps people relevant. Talent resilience ensures the business itself can continuously adapt. Businesses that combine both will move from experimentation to real enterprise productivity gains.

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The crypto market’s recent 0.67 per cent decline to a total capitalisation of US$2.29 trillion reflects more than routine volatility. It signals a decisive macro-driven repricing, with digital assets now moving in lockstep with traditional risk indicators. Over the past week, Bitcoin and the broader crypto complex have maintained a 64 per cent correlation with the S&P 500, a clear signal that rates-sensitive capital is treating crypto as part of the same risk bucket as equities. This is not a crypto-specific story. It is a story about liquidity, inflation expectations, and how geopolitical shocks transmit through every corner of the global financial system.

The primary catalyst for this selloff stems from a sharp spike in oil prices and a surging US dollar. Escalating Middle East tensions, including direct US–Iran conflict, pushed Brent crude above US$113.7 per barrel, its highest level since 2022. West Texas Intermediate followed, surging as much as 22 per cent to over US$111 a barrel at the open. Simultaneously, the US Dollar Index gained 0.6 per cent as investors fled to safety. This dual shock creates a powerful headwind for risk assets. Higher energy costs feed inflation expectations just as labour market data shows unexpected weakness, with 92,000 jobs lost in February. A stronger dollar tightens global liquidity conditions, making dollar-denominated assets more expensive for international holders and pressuring valuations across the board. Crypto, with its high beta and sensitivity to liquidity flows, feels this pressure acutely.

Bitcoin itself fell 2.03 per cent, contributing over half of the total decline in market cap. This move was not random. Large holders, often called whales, distributed coins they had recently accumulated, adding supply to an already nervous market. Spot Bitcoin ETFs saw net outflows, compounding the selling pressure. The Fear and Greed Index reading of 18, labeled Extreme Fear, confirms that sentiment has turned decisively negative. When sentiment reaches these extremes, technical levels gain outsized importance. Bitcoin now tests the US$66,000 to US$66,500 support zone. A sustained break below this range opens the path toward US$63,700. Bitcoin dominance holding above 58 per cent suggests capital is not rotating aggressively into altcoins, which typically underperform in risk-off environments. This concentration of weakness in Bitcoin, the market’s anchor, drags the entire ecosystem lower.

Also Read: While S&P 500 struggles, crypto’s low correlation to gold and stocks attracts institutional attention

The crypto selloff did not occur in isolation. Global markets moved in tandem, confirming the macro nature of the move. US equity futures plunged at the open, with Dow futures dropping over 800 points, roughly 1.8 per cent, and Nasdaq 100 futures sliding 1.9 per cent. Asian markets reflected similar stress, with the Nikkei 225 tumbling 6 per cent toward the 52,000 level, hitting an eight-week low amid Japan’s high dependence on Middle Eastern oil. Even gold, traditionally a safe haven, fell 1.4 per cent to US$5,099 an ounce in early spot trading, suggesting that liquidity needs are forcing investors to sell what they can, not just what they want to. This broad-based risk-off move underscores that crypto is no longer an island. It trades as part of a global macro tape, where oil, the dollar, and equity volatility set the tone.

Behind these price moves lie concrete geopolitical and economic fundamentals. Escalating hostilities involving Iran have effectively halted traffic through the Strait of Hormuz, a critical chokepoint for 20 per cent of global oil consumption. This disruption threatens to rekindle inflation fears just as central banks weigh their next moves. The market now prices in a 97 per cent chance that the Federal Reserve will hold interest rates steady at its March 18 meeting, with any potential cuts pushed back toward late 2026. This shift in expectations matters profoundly for crypto, which thrives in environments of easy money and declining real yields.

Adding to the uncertainty, corporate developments, such as BlackRock limiting withdrawals from its US$26 billion private credit fund, sparked contagion fears, causing its shares to tumble seven per cent. While Broadcom’s 4.8 per cent jump on bullish AI chip forecasts offered a rare bright spot, it was not enough to offset the broader risk aversion. Meanwhile, China’s decision to set its 2026 GDP growth target at 4.5 per cent to five per cent, the lowest in decades, signals ongoing deflationary pressures and trade tensions that further complicate the global outlook.

Also Read: Wallets, not smart contracts, were crypto’s biggest risk in 2025

Looking ahead, the near-term path for crypto hinges on two factors: oil price stability and the Federal Reserve’s tone on March 18. If energy markets calm and the Fed maintains a dovish stance despite inflationary pressures, crypto could find a floor near current levels. A sustained move above US$113 per barrel for oil would keep inflation expectations elevated, likely delaying rate cuts and maintaining pressure on risk assets.

Technically, Bitcoin’s ability to hold above US$66,000 remains the key level to watch. A decisive break below would likely trigger algorithmic selling and force leveraged positions to unwind, accelerating the move toward US$63,700. Traders should also monitor ETF flow data for signs of institutional accumulation or distribution, as these flows have become a reliable proxy for smart money sentiment in the current market structure.

This moment tests a core question for the crypto ecosystem: does it retain its narrative as an uncorrelated alternative asset, or has it matured into a risk-on instrument that trades with tech stocks and macro liquidity? Tell me about it. 

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

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Kampanat “Jom” Vimolnoht strode off the Singapore FinTech Festival stage in late 2024 with the kind of polish that sells trust: tailored blazer, crisp black T‑shirt, urbane charm.

To an audience of founders and investors, he was a familiar archetype: a crypto‑savvy venture capitalist with a UK Master’s in Investment Analysis, a history of venture capital roles and government advisory work, and a new post at KXVC, the corporate VC arm of Thailand’s leading Kasikornbank that had announced a US$100 million Web3 and AI fund the year before.

Also Read: Thailand’s corporate capital era: How big business became the startup banker

Only it was a mirage. Over the following year, a string of investors across Bangkok, Singapore, Ho Chi Minh City, and California discovered that the allocations, contracts, and deals Jom sold them were, in many cases, fabricated. The tale — reconstructed from interviews, bank transfers, blockchain traces, and KXVC’s own public warning — is both ordinary and devastating: ordinary because it follows familiar fraud mechanics; devastating because it exploited social capital — the halo effect of reputation — to drain life savings and corrode trust in a nascent investment ecosystem.

How the con unfolded

In private crypto markets, allocations to pre‑launch token sales are valuable and opaque. These deals typically circulate in invite‑only channels (Telegram, WhatsApp, and private investor lists) and access is concentrated among founders, funds, and a few insider intermediaries.

Vimolnoht played the insider role convincingly. He supplied professional‑looking decks, allocation agreements, and payment instructions, and invited friends and colleagues into deals in projects he claimed to be connected with: Monad, Babylon, Linera, and others.

Victims presented a consistent pattern: small initial transfers, followed by larger sums as trust deepened. One Bangkok executive, “Mark”, said he invested “in total more than a million dollars”. Another victim from the US, “Steven”, who paid in USDC, believes he lost about US$130,000, his life savings in crypto. Scamurai’s reporting identified about two dozen alleged victims so far, with individual losses ranging from roughly US$20,000 to over US$1,000,000. A blockchain wallet linked to some of the flows shows about US$1.71 million moving through it between July and October 2025.

When vesting milestones approached, and tokens were supposed to unlock, the excuses began: administrative delays, counterparty issues, even that Vimolnoht himself had been scammed.

Also Read: Inside the dark economy of crypto scams: 2024’s most lucrative fraud tactics

Communications then stopped. Project founders contacted directly by investors denied any affiliation or said they had only spoken informally with him. KXVC posted a terse advisory: it never raises external funds and has “never authorised any individual to act on behalf of KXVC” to solicit investor transfers to personal accounts. The firm has confirmed Vimolnoht left the company in March 2025.

A regional phenomenon

Thailand’s scams are not unique. What makes this episode instructive is how it exposes systemic vulnerabilities present across Southeast Asia: close‑knit networks, the prestige economy of panels and advisory roles, and a high appetite for outsized returns combined with uneven due diligence.

“It does not feel like being deceived. It feels like being trusted with an opportunity,” observed Dr Pun‑Arj Chairatana, former executive director at Thailand’s National Innovation Agency, warning that such schemes are often structured around curated deal‑flow circles and private chat groups that carry an air of exclusivity.

Prominent local voices corroborate the wider pattern. Yod Chinsupakul, CEO of local e-commerce company LINE Wongnai, noted the region’s “halo effect” for charismatic figures and urged a culture of whistleblowing to surface wrongdoing.

He also pointed to several other suspected malfeasances in Thailand’s tech sector: an e‑commerce enabler that collapsed amid alleged CEO fraud and tragic consequences for employees; a loyalty‑points startup accused of unlimited minting that reportedly cost a strategic partner “hundreds of millions of baht” (roughly US$5-15 million); and payment companies that allegedly engaged with illicit betting websites and opaque loans with potential conflicts of interest.

Chinsupakul stressed that while the proportion of bad actors has fallen since the industry’s early, “fluffy” years, the remaining wrongdoing is severe and often hidden.

The contagion effect

Dr Pun‑Arj cautions that the damage extends beyond direct victims. Reputation (the currency of fundraisings and syndications) erodes swiftly. He points to the Silicon Valley Bank collapse in 2023, not as an analogue in causation but as a lesson in contagion: a single failure can chill capital across markets. For small funds and emerging managers in Thailand and across Southeast Asia, the reputational fallout from prominent scams risks hamstringing legitimate teams still building track records.

This reputational contagion has practical consequences. Limited Partners revisit commitments; fundraising conversations stall; partnerships are re‑evaluated. Restoring confidence is not a matter of statements, Dr Pun‑Arj said, but of “consistency of conduct over time: transparent reporting, governance that is substantive rather than performative, and sound judgement exercised even when no one is watching.”

Enforcement, verification and the limits of charisma

Scammers exploit a familiar mix: technical plausibility (token deals, private allocations), social proof (panel appearances, advisory roles), and operational friction (the difficulty of verifying vesting schedules and off‑chain processes). That combination renders even seasoned professionals vulnerable.

What can change the calculus? Firms like KXVC have already issued public warnings; victims have filed police reports; journalists and whistleblowers are amplifying patterns. But systemic improvements are needed: clearer industry standards on syndication and disclosure, better investor education, escrow‑style mechanisms for pre‑sale allocations, and more robust checks on people representing institutional brands.

Also Read: From pig butchering to work-from-home scams, crypto crime has become more professional and global

Recent precedents in the region underline the point. In 2023-2024, Southeast Asia saw several high‑profile investment failures where founders or executives were accused of misappropriating funds or falsifying metrics, cases that left partner companies and retail investors nursing heavy losses and reputational wounds. These episodes reinforce Dr Pun‑Arj’s argument that governing conduct matters as much as technical sophistication.

Quotes that matter

KXVC’s warning is blunt and instructive: “Beware of imposters… KXVC has never raised funds from external sources and has not authorised any individual to act on its behalf in such manner.” It is a reminder that institutional identity can be weaponised in private markets.

Mark, one of the victims, captured the personal betrayal succinctly: “He worked at multiple VCs. He spoke on panels in Thailand, the US and Europe. It’s hard to understand.”

Opinion

Southeast Asia’s startup ecosystem has matured rapidly, but maturity requires not only capital and talent but also institutional hygiene. The Vimolnoht affair is a wake‑up call: charisma should never substitute for verification. Investors (institutional and retail alike) must demand paperwork that can be independently verified, insist on escrow or custodied arrangements for allocations, and treat personal introductions as the start, not the conclusion, of due diligence.

Regulators and industry bodies should tighten identity‑and‑representation norms for funds and require clearer disclosures on fundraising and allocation processes. Equally, platforms that facilitate private market deals must build safer rails: standardised contracts, provenance tracking for allocations and stronger remedies for victims.

Also Read: With new US$100M fund, KXVC aims to help global AI, deeptech, Web3 founders win APAC market

In short, the cure for confidence eroded by bad actors is not fewer deals, but smarter markets. Southeast Asia’s innovation boom can survive and thrive if stakeholders harden the institutional scaffolding that supports its convivial networking culture. Charm sells; proof secures. The region needs both, and, crucially, the latter must trump the former.

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