The path to becoming an AI-driven economy depends on how effectively industry, capital, and institutions can move together.

While the session covered topics ranging from autonomous mobility and AI-native banking to infrastructure and venture capital, the conversation consistently circled back to a broader question: how can Malaysia create the right ecosystem conditions for AI to scale meaningfully across the economy?

Moderated by Indranil Sarkar, Account Director, Startups at OpenAI, the panelists included:

Solving real-world problems

One of the key points raised during the session was that Malaysia does not need to win the global AI race by building the next OpenAI or inventing the biggest AI model in the world.

Instead, the bigger opportunity lies in applying AI to industries where Malaysia already has strong foundations and real economic demand. AI becomes far more valuable when it is plugged into real-world systems people already use every day.

In banking, this could mean changing how underserved SMEs and startups access financing. Instead of depending solely on traditional financial statements, AI systems are beginning to help banks assess alternative forms of business data and streamline underwriting processes for businesses that may have previously struggled to secure funding.

The same thinking also applies to how people move around cities. In a country where traffic jams, long commutes, and getting to public transport can still be a daily headache, autonomous vehicles (AVs) and smart transportation systems were discussed as potential ways to reduce travel friction, improve connectivity, and make urban mobility more seamless.

Beyond individual use cases, the panelists also pointed out that Malaysia already has several strengths that could support more localized AI innovation: from semiconductor packaging and data centres (IDCs) to tropical agriculture and its multilingual talent pool.

Rather than starting from scratch, the speakers suggested that the bigger opportunity lies in building AI around industries and capabilities the country already understands well.

Great technology alone will not build an AI ecosystem

Even the best technology means very little if the surrounding ecosystem is not ready for it.

Throughout the session, the conversation kept coming back to the same challenge: coordination.

Startups need funding and customers.
Corporates need trusted technology partners.
Investors want scalable business models.
Governments need safe and workable regulations.
And the market needs talent that can actually deploy AI in the real world.

That is why the discussion moved beyond just technology itself and into topics like regulation, commercialization, talent development, public-private collaboration, and trust.

The panelists also pointed out that many technologies already exist globally. The bigger challenge for emerging markets like Malaysia is how quickly they can adapt, localize, and scale them within their own ecosystem.

Building an AI ecosystem is not a one-player game.

It requires collaboration between startups, corporates, investors, regulators, and institutions that are willing to experiment, learn quickly, and support long-term growth.

More importantly, it requires all of these moving parts to work toward the same direction and create the right conditions for innovation to scale across the economy.

Acknowledgement

Special thanks to Indranil Sarkar, Account Director, Startups at OpenAI; Mohd Azharuddin Mat Sah, Executive Director at Sunway Group; Yichen (Chris) Mao, Executive Director at CICC Capital; and Kevin Shum Khai Yeap, Head of Digital SME Business Transformation & Merchant Acquiring at Alliance Bank Malaysia Berhad, for their thoughtful insights and contributions during the panel session at FutureX Ventures Fest 2026.

At FutureX Venture Fest 2026, organised by Sunway iLabs on the 12 May 2026, one statistic captured the mood perfectly. AI company Anthropic reportedly climbed from a valuation of around US$60 billion in early 2025 to approximately US$380 billion by 2026, one of the clearest signals that the AI race has triggered a modern-day gold rush in venture capital.

But beneath the excitement sits an older fear familiar to every experienced investor:

What happens when markets start moving faster than anyone can predict?

Lau described how investors are increasingly caught between FOMO (fear of missing out) and a distinctly Malaysian sense of kiasu: the fear of losing out while everyone else races ahead.

VenCap, a UK-based venture capital investment firm has backed leading venture funds across the United States, Europe and Asia for nearly four decades. For Cruttenden, who has invested through the dot-com crash, the financial crisis and now the AI boom, today’s market feels less like a normal technology cycle and more like a structural shift in human progress itself.

He described AI as an “absolute dislocation” comparable to the invention of steel.

And unlike previous technology waves, many of the companies shaping AI like OpenAI, Anthropic, Mistral, Cursor, Databricks and SpaceX are still private, placing venture capital firms at the centre of the transformation.

Yet one idea surfaced repeatedly throughout the session:

In chaotic markets, survival belongs less to whoever moves fastest, and more to whoever stays disciplined longest.

Betting Through Uncertainty

The challenge with AI is that nobody truly knows where the biggest long-term winners will emerge.

That uncertainty shapes how experienced investors think.

Cruttenden argued that the best venture investors are often less focused on predicting technologies themselves, and more focused on identifying the people who consistently spot transformative companies early.

For firms like VenCap, that has meant backing a concentrated group of venture funds with strong track records across US, Europe and Asia; a strategy that provided early exposure to companies such as Google, Meta, ByteDance, Databricks, OpenAI and Anthropic.

That philosophy is rooted in what investors call the power law: where a tiny number of companies generate the overwhelming majority of returns.

According to Cruttenden, roughly 1% of venture-backed exits create half the value across the entire market. The difficulty is that nobody can consistently predict where those outliers will emerge, especially during moments of technological mania.

Which is why many experienced investors avoid overcommitting to a single dominant model or platform too early. Instead, the strategy increasingly resembles a diversified “basket approach” across infrastructure, developer tools, applications and AI agents.

Cruttenden repeatedly returned to lessons from the dot-com crash, when investors chased growth aggressively before the market eventually collapsed.

The goal, he argued, is not to avoid risk.

Venture capital depends on risk-taking.

The objective instead is to avoid becoming trapped when excitement begins outrunning reality.

In chaotic markets, diversification is not hesitation.

It is survival.

No Shortcuts for Southeast Asia

The conversation eventually turned toward Southeast Asia’s ambition to build globally competitive technology ecosystems.

Silicon Valley’s advantage was never built on capital alone. It emerged over decades through a combination of entrepreneurial culture, supportive regulation, institutional funding and repeated startup successes that created a self-reinforcing flywheel.

That kind of ecosystem takes time to develop.

The discussion also pushed back against one of the most common assumptions across emerging markets, that the main problem is a lack of growth capital. More often than not, the challenge is building enough companies capable of scaling into truly world-class businesses.

In the end, strong ecosystems are not created overnight through funding alone. They are built gradually through supportive systems, risk-taking culture and founders capable of creating the next generation of breakout companies.

Acknowledgements

Special thanks to Tim Cruttenden, Chief Executive Officer of VenCap, and Karen Lau, Chief Operations Officer of Sunway iLabs, for their thoughtful insights and contributions during the fireside chat at FutureX Venture Fest 2026.

AI capabilities are advancing rapidly across diagnostics, workflows, and patient care, yet much of healthcare still operates on fragmented systems, outdated infrastructure, and reactive models of care.

Most healthcare systems today were built to respond to illness after it occurs, while AI is increasingly shifting conversations in healthcare toward more preventive, personalised, and outcome-oriented approaches to care. But meaningful adoption may require more than simply layering AI tools onto existing systems.

Moderated by Nur Syimah Izzah Abdullah Thani, Head of Corporate Affairs at AstraZeneca Malaysia, the session brought together perspectives from healthcare AI, venture capital, and digital health to explore how healthcare systems may need to evolve alongside the rapid pace of AI development.

When Technology Moves Faster Than Healthcare Systems

Healthcare is often trying to integrate AI into systems that were never originally designed for it.

Despite rapid advances in diagnostics and workflow automation, many healthcare providers still rely heavily on disconnected operational processes, siloed data systems, and workflows built years ago.

One of the panelists compared this challenge to building advanced highways on top of broken roads, where the limiting factor is no longer the technology itself, but the surrounding infrastructure and the way healthcare delivery is currently organised.

This extends beyond hospitals and into the patient experience itself.

Many patients still navigate healthcare through fragmented pathways, relying on referrals, assumptions, or trial and error to determine where to seek treatment.

AI could eventually support more connected and coordinated care pathways, like a “Waze for healthcare,” helping guide patients toward appropriate care based on symptoms, medical history, and healthcare outcomes.

Building the Foundations for Scalable AI

As the conversation shifted toward what would be needed to support AI adoption, two key areas were repeatedly highlighted: the importance of better-connected healthcare systems and the need to make future healthcare models more accessible and scalable.

Connected Healthcare Systems and Shared Data

Since data across the healthcare ecosystem remains deeply fragmented, patient records often do not move seamlessly across providers, limiting the ability of AI systems to learn from complete patient journeys and long-term patient data.

Without interoperable infrastructure and shared data standards, the ability for AI systems to scale meaningfully across healthcare environments remains limited.

Countries such as Indonesia and the UK were highlighted as examples of how governments are approaching this challenge. Indonesia’s SATUSEHAT initiative was highlighted for its efforts to integrate healthcare records across providers through shared standards, while the UK’s NHS has invested heavily in genomic profiling and polygenic risk scoring to support more preventive and precision-based approaches to healthcare.

These reflect a growing recognition that healthcare systems need to exchange data more seamlessly for AI technologies to be adopted meaningfully at scale.

Making AI-enabled Healthcare Accessible

The speakers also discussed how more affordable and accessible healthcare models could help widen participation while generating the real-world data needed to improve future AI-driven healthcare systems.

Healthcare costs continue to rise globally, with healthcare expenditure estimated at approximately US$10 trillion annually, while a substantial portion of healthcare resources may still be lost to inefficiencies, fragmented systems, and gaps in care coordination.

Against that backdrop, the panel explored whether healthcare could eventually move toward more accessible subscription-style models, similar to how platforms such as Netflix popularised continuous access through simple, low-barrier recurring pricing models.

The idea was not necessarily about replicating streaming platforms themselves, but about creating healthcare systems that encourage more consistent participation rather than patients only engaging with the system when serious illness occurs.

Broader participation could generate richer real-world healthcare data over time, potentially supporting more personalised care pathways and improving the scalability of future AI-driven healthcare tools.

Trust, Adoption and the Human Factor

Yet even with stronger infrastructure and scalable systems, adoption ultimately depends on people as much as technology.

Healthcare remains an industry built heavily on trust, expertise, and established workflows.

As AI systems become more capable, adoption will depend not only on technological performance, but also on whether healthcare professionals feel confident integrating these tools into clinical practice.

The importance of measurable outcomes, clinical validation, and careful integration into existing healthcare environments was repeatedly emphasised as key factors in building trust and encouraging adoption across healthcare settings. Without it, even highly capable technologies may struggle to achieve widespread adoption.

Ultimately, this painted a picture of a healthcare industry navigating a period of significant transition.

Because in the AI era, healthcare transformation is becoming as much a systems challenge as it is a technology one, requiring not only new tools and infrastructure, but also a willingness to remain open to change.

Acknowledgements

Special thanks to Teodor Stoev, Regional Director, Asia at Harrison.ai; Lau Shi Ying, Managing Partner at Adaptive Capital Partners; Dr. Raymond Choy, Founder & CEO of Heydoc Health; and Nur Syimah Izzah Abdullah Thani, Head of Corporate Affairs at AstraZeneca Malaysia, for their thoughtful insights and contributions during the panel session at FutureX Ventures Fest 2026.

Moderated by Thomas Jeng, Head of Startups, APAC of OpenAI and featuring Michel Del Buono, the Operating Partner & Chief Investment Officer of Andreessen Horowitz (a16z Perennial), the two discussed about what happens when AI fundamentally changes the economics of building, scaling, and competing.

Underneath nearly every point raised during the session was one core idea:

Software is becoming dramatically cheaper to create.

The Collapse of Software Costs

The most important shift discussed during the session was not AI itself, but what AI is doing to the economics of building software.

According to Del Buono, some teams have become as much as 20 times more productive in just the last six months. Tasks that previously required entire engineering teams can now be handled by a few people using AI coding agents and automated workflows.

That productivity jump is beginning to reshape the financial assumptions of the startup ecosystem from the ground up.

One concept that emerged from the discussion was what Del Buono described as “disposable software.” Instead of building permanent applications, developers are increasingly creating software for highly specific use cases, deploying it quickly, and abandoning it once the task is complete.

He shared an example involving his son, who built a tool that scanned academic papers across universities, generated personalised outreach emails to professors, tracked responses, and automated parts of the internship application process. The software served its purpose once and was never needed again.

Software, in other words, is starting to behave more like a temporary workflow.

And once software becomes cheap to create, the effects begin cascading outward.

The Rise of the Lean Titan

One downstream effect is the emergence of what could be described as the “lean titan”: highly efficient startups operating with minimal overhead.

The AI startup ecosystem is increasingly splitting into two extremes.

On one side are infrastructure giants raising enormous amounts of capital to build foundational models, compute infrastructure, and tooling layers. On the other are tiny AI-native teams building profitable businesses without needing much funding at all.

More founders are choosing to stay bootstrapped for longer periods of time. With AI dramatically reducing development costs, some are reaching multi-million-dollar revenue run rates while maintaining tiny teams and retaining full ownership control.

For ecosystems outside Silicon Valley, this shift could prove significant.

Historically, access to deep capital markets gave certain regions a structural advantage in building globally competitive software companies. But if AI continues reducing the cost of execution, talented founders in Southeast Asia and beyond may no longer need Silicon Valley-scale funding to compete internationally.

The economics of ambition begin to change.

The New Moat Is Judgement

As software creation becomes increasingly commoditised, another question naturally follows:

What actually becomes defensible?

The answer, increasingly, is not the code itself.

It is judgment.

During the discussion, both speakers pointed toward a future where the strongest companies are the ones best able to orchestrate multiple models together depending on the task: optimising for reasoning quality, latency, workflow performance, or cost efficiency.

But the deeper advantage lies in knowing what to optimise for in the first place.

AI can generate the first 80% remarkably quickly. But the final layer — knowing what matters, what feels right, what to optimise for, and how to refine the output — still depends heavily on human expertise.

In an environment where software becomes abundant, taste starts becoming infrastructure.

When Historic Valuation Models Break

The conversation eventually moved beyond startups and into a more difficult question for investors and policymakers alike:

How do you value the future when historical comparisons stop working?

Jeng noted that traditional economic frameworks are generally built around first-order effects: relatively predictable outcomes that emerge from gradual change. That logic works reasonably well when industries evolve incrementally.

AI may not.

Building on that point, Del Buono used autonomous driving to explain how transformative technologies create second- and third-order effects that are difficult to predict ahead of time.

The first-order effect is obvious: self-driving cars make transportation more efficient.

But the bigger changes come after that.

If people can sleep or work while commuting, they may become more willing to live two or three hours outside city centres. Over time, that could reshape urban planning, property values, transportation systems, and even insurance markets if accidents become less common.

In other words, the technology doesn’t just improve driving. It changes human behaviour, and that creates ripple effects across entire industries.

For investors, that makes valuation unusually difficult.

Traditional investment models rely heavily on historical comparisons. But with AI, many of the biggest economic effects may only emerge later through cascading second- and third-order consequences that are still impossible to fully measure today.

Time to Start Tinkering

The conversation also touched briefly on regulation, where Del Buono compared AI to the spread of the printing press centuries ago.

When printing presses were rare, information was easier to control because there were only a handful of people producing it. But as the technology spread and printing became widely accessible, centralised control became far more difficult.

AI may already be entering a similar phase.

Just six to twelve months ago, many assumed advanced AI systems would remain concentrated within a handful of major companies because the technology seemed too expensive and complex for ordinary people to run themselves. Today, open-source models are already being run locally on personal machines.

Once a technology becomes cheap, widespread, and accessible to everyone, controlling it becomes far more difficult.

Open-source models are becoming more powerful and increasingly accessible on personal machines, making the technology harder to centralise or contain. The broader implication was not that regulation no longer matters, but that adaptation may move faster than policy itself.

And that may be why both speakers ultimately arrived at the same conclusion:

The best way to understand AI is to engage with it directly.

Experiment with the tools.
Build things.
Learn by doing.

Acknowledgements

Special thanks to Michel Del Buono, Operating Partner & Chief Investment Officer of Andreessen Horowitz (a16z Perennial), and Thomas Jeng, Head of Startups, APAC at OpenAI, for their thoughtful insights and contributions during the fireside chat at FutureX Ventures Fest 2026.

Is SaaS actually under threat? Or are we misunderstanding what’s changing?

The shift is subtle, but important

For years, SaaS followed a simple model. You built software that helped people do their work, and you charged them per seat. More users meant more revenue.

AI starts to change that relationship.

Software is no longer just helping with the work. It is beginning to do parts of the work itself.

AI agents can autonomously perform complex tasks, diminishing the need for multiple specialized SaaS applications.

That sounds obvious, but it changes how value is perceived. If the outcome becomes easier to deliver, the product itself becomes easier to question.

A simple example: legal tech

Take legal software.

Traditional SaaS companies have built tools to help lawyers manage documents, collaborate, and track workflows. These tools improve efficiency, but the work still sits with the lawyer.

Now look at Harvey AI.

Harvey doesn’t just organise work. It can draft documents, review contracts, and support legal reasoning. The focus shifts from managing the process to shaping the outcome.

With AI, software is moving from being a tool to becoming a worker.

That difference starts to matter commercially. One model charges for access. The other is much closer to charging for results.

What is actually changing

This is where much of the SaaSpocalypse discussion comes from.

Pricing is the first pressure point. If fewer people are needed to do the same work, per-seat pricing becomes harder to defend. Buyers begin to ask what they are actually getting, not just how many licences they need.

At the same time, it has become much easier to build and replicate features. What used to feel differentiated can now be recreated quickly. This creates the impression that software is becoming commoditised.

But that is only part of the picture.

What is not changing

Software companies were never valuable just because of their code.

They became valuable because they sat inside important workflows, accumulated data over time, and built trust with their users. Those things are still hard to replicate.

If anything, AI makes them more important.

This is why many incumbents are not disappearing overnight. And it is also why new entrants succeed when they go deep into a specific workflow, rather than building something generic.

A clearer way to think about it

The term SaaSpocalypse suggests an ending.

What is actually happening feels more like a shift.

We are moving from software that supports work to software that participates in it. That changes how products are built, how they are priced, and where defensibility comes from.

For founders, the implication is quite practical.

SaaS companies need to rethink how they deliver value, not just add AI features on top. AI startups need to go beyond demos and build systems that customers genuinely rely on.

This is not just a race to adopt AI, but to rebuild around it and redefine where the moat lives.

That shift is already showing up in the traditional SaaS pricing model, with many in the industry pointing to a move away from per-seat licensing toward consumption-based approaches, closer to prepaid tokens than fixed licences.

So, is the SaaSpocalypse real?

In some ways, yes.

Some pricing models will struggle. Some products will lose their edge. Certain categories may shrink.

But software itself is not going away.

It is moving closer to the outcome.

In places where traffic is part of everyday life, driving is rarely just about getting from one place to another. It’s time spent waiting, watching, and staying alert. And during festive seasons, it only intensifies. Just like during the recent Hari Raya Aidilfitri, when highways filled up as millions travelled, with multiple road incidents further worsening congestion across major routes.

But what if that time didn’t have to be spent driving at all? That’s the idea behind autonomous vehicles (AVs).

Whether it’s catching up on work, reading the news, or just taking a breather before the next part of the day. It’s a small shift in concept that could have a very different impact on how land mobility is experienced, especially for those who don’t have the option to drive in the first place.

Recently, Sunway iLabs sat down with Mohd Azharuddin Mat Sah, a prominent figure in Malaysia’s mobility and public transport sector: former CEO & President of Prasarana, and now Executive Director of Sunway Group President’s Office, to explore what that future could really look like.

He points out that autonomous vehicles have the potential to reshape how mobility evolves. Just a century ago, transport was built out of necessity, not foresight. AVs today sit in a similar place, with the prospect to reshape our cities and modern suburbs, only this time, with the opportunity to plan ahead.

How do Autonomous Vehicles Work?

At a basic level, an AV system is designed to replicate what a human driver does. It relies on a combination of sensors such as cameras, radar, and lidar, as well as artificial intelligence (AI) to interpret road conditions, detect obstacles, and make decisions in real-time.

Those decisions are then put into action, like adjusting steering, accelerating or slowing down, and braking when needed to navigate traffic safely.

But we still have a long way from adopting fully autonomous Level 5 vehicles.

Most systems today fall within a defined spectrum of automation. Today’s AVs, primarily Level 2 and Level 3 systems, still require human oversight, where drivers can briefly take their attention off the road. Meanwhile level 4 systems operate without human drivers but only within specific areas or conditions.

Because developing autonomous systems is complex and expensive, AVs today are mostly deployed as commercial services rather than personal use.

Why AVs are Gaining Attention

When cars first went mainstream, they gave people the freedom to move on their own terms, yet every trip demands attention, whether stuck in traffic or cruising on the highway. AVs are trying to keep the freedom, but take away the need to constantly focus on driving.

For all the attention on the technology itself, the real question is: what problem does it solve?

Greater Road Safety

Safety stands out as one of the most immediate values, a point also raised by Azharuddin.

Road safety remains a significant concern. In Malaysia, more than 3,000 road fatalities were recorded in 2025, highlighting the ongoing risks faced on the road and the need for safer solutions.

Many of these incidents stem from human error: distraction, fatigue, or split-second misjudgements. AVs attempt to address this by shifting decision-making to systems that don’t get tired or distracted. Systems that are designed to follow road rules, reducing risks that most commonly lead to accidents today.

Operational Efficiency

But beyond safety, there are also operational pressures shaping adoption. Across public transport, logistics, and mobility services, there is an increasing reliance on human drivers, along with growing challenges in maintaining that workforce. Self-driving systems offer a way to ease that dependency while improving efficiency over time. Some of the most immediate applications include:

• Robotaxis – providing on-demand ride-hailing services, similar to Grab or Uber

• Autonomous shuttles – providing fixed-route passenger transport in areas like campuses, townships and transit hubs

• Autonomous trucks – supporting long-haul logistics movement

• Autonomous service vehicles - supporting urban maintenance, such as robosweepers for sanitation

Where Progress Meets Friction

For all the progress being made, autonomous vehicles are still far from widespread adoption. The biggest hurdle today is regulation.

In motion, under trial

In Malaysia, AV development has already begun moving onto public roads. Guided by the Ministry of Transport’s (MOT) Guideline for Public Road Trials of Autonomous Vehicles and the National Regulatory Sandbox, pilot programmes have been introduced to test how AV systems perform in urban environments.

Over the past few years, several initiatives have taken shape: from the MyAV testing routes in Cyberjaya first approved in 2020, to autonomous bus trials along Sunway BRT in 2022, and subsequent trials in Putrajaya in 2024. These projects test how AV systems perform in real-world conditions, while giving regulators the data they need to move forward.

But for the most part, they still remain in the pilot phase. A full legal framework for widespread deployment has yet to be put in place, and the reason comes back to regulation.

The road to scale: bound by rules

Moving from trials to everyday use means going through multiple layers of approval—across federal agencies like the MOT, Road Transport Department (JPJ), Land Public Transport Agency (APAD), and Malaysian Institute of Road Safety Research (MIROS), as well as local authorities responsible for specific roads and environments. Current frameworks are designed to support testing, not full-scale deployment.

At the centre of it all is a fundamental question: how safe is safe enough?

As Azharuddin puts it “the key lies in how robust the system is, whether it can assess its surroundings and respond appropriately”.

Because deploying AVs isn’t just about whether a vehicle can operate on its own. It’s about whether it can do so consistently in real-world conditions. That includes handling heavy rain, unclear road markings, and unpredictable traffic behaviour, environmental factors that are part of everyday driving.

It also goes beyond the vehicle itself. Self-driving systems rely on a wider ecosystem: connectivity, infrastructure, and communication between vehicles and their surroundings.

There is also still unresolved questions of liability and insurance: who is responsible when something goes wrong—the human, or the system?

Traditionally, accidents are judged by negligence, where a driver is held responsible for failing to exercise reasonable care. In contrast, if a vehicle is deemed inherently unsafe, liability can shift to the manufacturer.

But AVs blur this distinction. Countries like the United Kingdom are already taking steps to address this through the Automated and Electric Vehicles Act 2018 (AEVA) and the Automated Vehicles Act 2024 (AVA).

While the UK is still finalizing the detailed operational framework needed for full implementation, the legal groundwork is clear: these laws extend insurance to cover autonomous driving, and when a vehicle is operating in authorised self-driving mode, responsibility shifts away from the human occupant to the companies behind the system.

For now, the technology is advancing, but the system around it is still catching up.

Much of the early AV development in Malaysia has been driven by players like eMoovit, a local startup focused on building software that can turn different types of vehicles into autonomous systems for urban use.

But beyond Malaysia, the technology is already being rolled out at scale.

Deployment: From the East to the West

Adoption across markets, however, is not one-size-fits-all. Vehicles must align with local road systems, such as left- or right-hand drive (LHD/RHD), as well as local environmental conditions, such as weather and road quality, factors that shape where AVs can realistically operate.

Globally, companies like Waymo, Zoox, Baidu, WeRide, and Pony.ai are already deploying autonomous systems across different markets. BCG predicts that the United States and China are expected to dominate the initial growth of AVs, driven by supportive regulations, infrastructure investments, and market conditions.

In the United States, companies like Waymo and Zoox are already pushing AVs into real-world use. Waymo, for instance, is now providing around 500,000 paid robotaxi rides every week across 10 cities, while Zoox, has served over 300,000 riders across Las Vegas and San Francisco, showing that self-driving transport can operate as a commercial service.

In China, companies like Baidu and WeRide are scaling rapidly with strong policy support, backed by national initiatives that prioritise AV development through R&D funding, streamlined approvals, and infrastructure built to accommodate autonomous systems.

Baidu’s Apollo Go has reached over 300,000 weekly rides, with expansion beyond China into markets like South Korea, and WeRide, on the other hand, now operates a global robotaxi fleet of over 1,000 vehicles across multiple cities, including commercial operations in places like Guangzhou and Abu Dhabi.

Singapore has also taken a more structured approach, with regulatory frameworks and pilot initiatives backed by agencies like the Land Transport Authority (LTA) and NTU’s CETRAN, creating an environment for AV testing and deployment. This has helped attract companies like WeRide and Pony.ai to test and gradually commercialise their services, including Grab-WeRide’s Ai.R autonomous shuttle service, which recently began public operations in Punggol.

Where This Leaves Us

Autonomous vehicles are not just about replacing drivers, the value lies in improving safety and efficiency, while also opening up new opportunities across the land mobility ecosystem.

As Azharuddin highlighted, “It’s not about taking jobs away. Instead of driving, people can be managing systems, working with data, and building skills that are more relevant in today’s AI-driven world.”

Over time, this could contribute to a more connected, efficient and sustainable urban future.

With plans underway to establish Malaysia’s own legal framework for AVs, and companies like Pos Malaysia already exploring real-world applications through initiatives like the six-month autonomous logistics vehicle trial, it suggests that AVs may soon become part of our everyday reality.

Update: Since this article was published on 28 April 2026, China has reportedly suspended the issuance of new autonomous-driving permits following the outage involving Baidu’s Apollo Go robotaxis in Wuhan on 31 March 2026, where more than 100 vehicles stalled on city streets. (China suspends autonomous driving permits after Baidu outage | The Star) 

References

Allianz. (2025). Allianz Motor Day 2025: HANDS OFF – The Safety Promise of Autonomous Driving.

Automated and Electric Vehicles Act 2018. (2018). Legislation.gov.uk.

Automated Vehicles Act 2024. (2024). Legislation.gov.uk.

Digital News Asia. (2020). Now it gets real – Malaysia approves first autonomous vehicle testing routes in Cyberjaya by Futurise and Ministry of Transport.

Hagenmaier, M., Pfaffinger, S., Bert, J., Wegscheider, A. K., Cui, D., Lang, N., Schaber, L., & Brunner, R. (2026). Here at Last: The Evolution of the Robotaxi. BCG Global.

Kalbana Perimbanayagam. (2026). Accidents, heavy traffic slow major highways as Malaysians return from Aidilfitri. NST Online; New Straits Times.

Kang, L. (2026a). WeRide robotaxi fleet surpasses 1,000 vehicles. CnEVPost.

Kang, L. (2026b). Baidu’s Apollo Go robotaxi hits 300,000 weekly rides as service expands to South Korea. CnEVPost.

Korosec, K. (2026). Waymo’s skyrocketing ridership in one chart | TechCrunch. TechCrunch.

Malay Mail. (2025). Transport Ministry: Over 3,000 road fatalities recorded in Malaysia in first half of the year. Malay Mail ; Malay Mail.

Ministry of Transport Malaysia & Futurise Sdn Bhd. (2024). Guideline for Public Road Trials of Autonomous Vehicles.

Palmer, A. (2026). Amazon’s Zoox expands robotaxi testing to Phoenix and Dallas. CNBC.

Tjoe, L. N. (2026, April). Free autonomous shuttle rides launch in Punggol. The Straits Times.

Our team at Sunway iLabs has been closely following the evolving robotics landscape. Signals are becoming clearer: robots are steadily moving beyond research labs and controlled demonstrations into real operational environments. Even industry leaders like NVIDIA CEO Jensen Huang has suggested that robots could be only a few years away from widespread use.

As more robotics companies enter the field and promise similar capabilities, how do we determine which robot actually makes sense for a given use case? The situation is not unlike the early days of smartphones, when dozens of devices appeared with seemingly similar features. The real question then becomes:

What should we really look for when evaluating a robot?

How to Choose a Robot

Let’s take a look at some of the key criteria to consider when surveying the robotics landscape: the factors that help determine whether a robot is truly ready for real-world deployment, or just an impressive demo. To make sense of this, it helps to evaluate robots across a few key questions: how well they understand their environment, how they move, whether they can operate reliably over time, and whether they are already being used in real-world settings.

Embodied Intelligence

The first test of any robot is intelligence: does it understand its surroundings, respond appropriately, and adapt in variable conditions in the environment it is meant for?

The emergence of AI has marked a shift in robotics. Embodied AI combines intelligence with physical structure—the robot’s body, sensors and surroundings—so that it can perceive, decide, and act in the real world.

Rather than solely relying on fixed data, these systems are also trained extensively in simulated environments. This allows robots to learn faster and at scale, while continued learning from real-world deployment improves how well they adapt to changing conditions and handle tasks that require both precision and coordination.

Platforms such as AgiBot’s Real-World Reinforcement Learning (RW-RL) system combine large-scale pre-collected data with on-site training. It allows their robots to learn tasks directly on the factory floor within minutes and adapt to changing production conditions without major reconfiguration, marking a step toward intelligence that improves through real-world use rather than solely relying on fixed instructions.

This shift is not limited to a single company. Other leading full-stack robotics players are also moving in the same direction, overcoming one of robotics’ biggest constraints: the difficulty of collecting enough data to train systems effectively.

By combining simulation and real-world learning, they reduce reliance on slow and costly data collection while improving how quickly robots can be deployed and adapted in practice.

Progress in robotics is increasingly defined not just by hardware performance, but by how well systems can operate in semi-structured or unstructured environments, where situations can be unpredictable, tasks vary, and decisions must be made in real-time.

In other words, look for a robot meant for messy reality, one that can handle unpredictable human behavior, adapt to dynamic settings, recover safely from errors, and improve continuously through experience.

Mobility

Once a robot can decide what to do, the next question is whether it can physically do it.

Mobility is more than whether a robot can walk. It includes balance, payload capacity, movement speed, joint coordination, and how well the machine handles obstacles such as slopes, stairs, tight corners, or uneven ground.

Recent demonstrations, such as Unitree’s G1 humanoid performing complex martial arts movements offer a useful way to interpret these capabilities. While such demos may appear theatrical, they reveal underlying attributes that matter in real-world use, including dynamic balance, precise coordination across multiple joints and the ability to maintain stability while executing fast, continuous movements.

These capabilities are reflected in the robot’s design. The G1 has 43 degrees of freedom, allowing for fine-grained control across its body, while its high joint torque enables it to generate and withstand force during movement.

Its dexterous hands also matter because mobility is not only about locomotion, but about whether the robot has the flexibility and control to manipulate objects and carry out specific tasks. Hand design, force control and degrees of freedom help indicate how precisely it can grasp, hold, and handle objects once in position.

Details like these offer useful clues about whether the robot is meant to fit into specific environments. When evaluating mobility, ask whether the robot’s movement capabilities actually match the physical conditions of the job.

Operational Readiness

A capable robot still needs to operate as part of a real-world system.

Operational readiness is about whether the robot can run consistently within the constraints of your workflow. This includes practical factors such as battery life, charging time, uptime, maintenance requirements, availability of spare parts and support and how the robot handles failures or interruptions.

A robot that performs well in controlled settings but cannot be supported or maintained efficiently may not yet be ready for real deployment. For instance, a robot that operates for two hours but requires four hours to recharge may struggle to sustain continuous operations without additional units or human intervention.

Though often overlooked, this factor ensures whether a robot is practical or just impressive. UBTECH’s Walker S2, for example, is designed to swap its own battery pack and continue operating with minimal downtime.

When evaluating operational readiness, consider whether the robot can run consistently, be maintained efficiently, and operate as part of a larger system.

Real-World Adoption

Even the most advanced robot means little if it hasn’t proven itself outside the lab. It’s important to look at whether the robots being evaluated are already deployed in real environments, who their customers are, and whether those use cases resemble your own needs.

Service robots are already operating in everyday settings. Companies such as Pudu Robotics have deployed robots across restaurants, retail stores, hotels and healthcare facilities worldwide. These robots assist diners in places from Canada and Hong Kong to Malaysia, while others operate in shopping malls across Poland, Thailand and Jordan.

UBTECH has also announced plans to scale production from hundreds to several thousand units over the next few years with automotive manufacturers such as BYD, Geely Auto, FAW-Volkswagen, and Dongfeng Liuzhou Motor beginning to integrate its robots into industrial operations for round-the-clock use.

These figures indicate how quickly robots are moving from experimental prototypes toward commercial deployment. Look for signs of real-world traction: how many units have been shipped, whether production is scaling up, and whether the robots are being used by real organisations.

Where This Leaves Us

Robots are useful because they can take on routine tasks with consistency and minimal supervision. With companies ramping up production and preparing for wider deployment, robots are beginning to show up across a growing number of settings, from service environments to industrial operations: in different forms, for different kinds of work.

So if you are considering a robot, don’t forget to ask a few fundamental questions:

1. Can it move reliably in your environment?

2. Can it understand and respond to what’s happening around it?

3. Can it operate consistently within your workflow over time?

4. Is it already being used outside the lab?

The best robot is not the most futuristic one; it is the one whose capabilities match the job you need done.

Acknowledgements: Thank you to the Sunway iLabs team for their invaluable contribution and insights in preparing this article.

References

FutureX Insights. (2026). Robots Among Us. Futurexinsights.news. https://www.futurexinsights.news/p/robots-among-us?r=6i8ih6&utm_campaign=post&utm_medium=web

Jijo Malayil. (2025, December 30). UBTECH robot dances, kicks box as 1,000 Walker S2 milestone marked. Interesting Engineering. https://interestingengineering.com/ai-robotics/china-ubtech-builds-1000-humanoid-robots

South China Morning Post. (2025, November 19). South China Morning Post. https://www.scmp.com/tech/article/3333427/ubtechs-2026-humanoid-robot-output-grow-10-fold-costs-plunge-scale-economics

‌

‌

But look at what just happened during the humanoid robot showcase at China’s state broadcaster, CCTV’s Spring Festival Gala. Chinese firm Unitree Robotics stunned audiences with fluid movements, agility, and impressive martial arts performance on stage alongside young human artists during a live national broadcast.

A stark contrast to the stiff dance routines we saw just a year ago in 2025, this performance signals how quickly humanoid capabilities are evolving.

Now, we shift the lens. As we move through 2026, the industry is no longer just fighting over how robots move; it’s fighting over how they think.

The Brain and the Body

At a basic level, many modern robots can be understood through two parts: a brain and a body.

The brain is the thinking part. It’s what lets a robot see the world, make decisions, and plan its next move. It’s made up of the AI models, software, and computing systems that turns data from sensors into action.

The body is the doing part. It’s the motors, sensors, batteries, connectors and mechanical components that enable movement, balance and interaction with the real world.

This distinction shows that not all robotics companies build the same thing. Some focus entirely on the brain, while others build full-stack systems, tightly integrating the brain with the body to deliver complete robots.

Selling the Universe Mind - The “Android” Play:

If the last decade was about mechanical engineering, 2026 is about Foundational Intelligence. A growing faction of the industry argues that the physical hardware is a solved problem; the real bottleneck is the “ghost” in the machine — the intelligence.

Brain Providers are the Google of this new era. They don’t want to build the metal, they want to build the Operating System that runs on everyone else’s hardware. That’s where some companies are placing their bets.

Companies like Skild AI and Physical Intelligence are building foundational models for the physical world. Their goal? A universal “robot brain” that can be dropped into a forklift, a humanoid, or a robotic arm and just work.

Others, such as PrismaX, work on the data and learning layer that helps robots improve through real-world use. Even firms like HappyRobot AI show how powerful software systems can automate complex work without being tied to a specific machine.

It’s similar to how NVIDIA powered the AI boom. Companies like OpenAI, Anthropic, and Meta, run their large language models (LLMs) on NVIDIA chips. Different applications, same underlying layer. Brain providers in robotics are aiming to become that shared intelligence layer, powering many different machines.

The Full-Stack Titans: The “iPhone” of Robotics

While the brain camp bets on a universal OS, the full-stack builders are playing a different game entirely. They aren’t just writing the software, they are forging the steel. This is the Apple model of robotics: a closed-loop system where the “Mind” and the “Machine” are designed as a single, inseparable unit.

Some companies choose to design and build both brain and body together, tightly integrating AI with hardware from the start. These full-stack builders control the entire system, from the motors and mechanics to the software that drives them.

Companies like Figure AI, Apptronik, UBTECH, Unitree, LimX Dynamics, and Fourier Intelligence fall into this category. They design robots where intelligence and physical design evolve side by side. The primary advantage of this approach is total optimisation:

• Lightning-Fast Reflexes: In the physical world, a split-second delay is the difference between a robot catching a falling object or causing an expensive accident. By building both the sensors and the software, these companies achieve a level of precision that universal brains can’t match.

• Physical Intuition: These builders argue that true intelligence must be “embodied.” This means the AI isn’t just a generic program, it intimately understands the unique physics and “muscles” of its own custom-built body.

When the same company builds both the body and the brain, it can optimise performance, balance, and behaviour as one cohesive system rather than stitching mismatched components together. If brain providers aim to create reusable intelligence platforms, full-stack builders aim to deliver complete, turnkey machines ready to operate in the real world.

The Missing Middle: The Data Pipeline

In 2026, the real bottleneck isn’t the “Brain” or the “Body”, it’s the data pipeline connecting them. Without Robot Operations (RobOps), even the most advanced machine is just an expensive paperweight.

Data is the new oil, and the industry is drilling for it in two ways:

• Synthetic Data: To bypass the slow, costly process of real-world training, companies use hyper-realistic simulations. Here, robots practice tasks millions of times in seconds before touching a factory floor.

• Data Sovereignty: As robots enter homes and hospitals, privacy is paramount. Firms like PrismaX are leading the charge in ensuring that the sensitive data robots collect remains secure and private.

While Silicon Valley perfects the “Mind,” China is building the infrastructure to feed it. By leveraging its massive manufacturing footprint and deploying “cyber-labourers”, human operators in VR rigs who act out tasks for AI to learn, China has become the world’s largest sandbox for RobOps.

Robotics: Ready or Not?

Big players in the industry suggest that robotics is approaching a breakthrough, though more cautious voices point to the constraints.

At CES 2026, NVIDIA’s Jensen Huang said the “ChatGPT moment for robotics is nearly here,” signalling that advances in AI could soon unlock a step change in robotics capability.

Yet Morgan Stanley’s 2026 report offers a grounded reminder, that robots today remain costly. Wider adoption will depend on hardware becoming cheaper, systems becoming more reliable, and AI models improving enough to operate consistently beyond controlled settings.

Where This Leaves Us

The robotics industry is currently in its “dial-up” phase; it is functional but waiting for its broadband moment. Whether the winner is a universal “Android-style” Brain or a specialised “Apple-style” Full-Stack machine, the prize is the same: the automation of physical labour.

Acknowledgements: Thank you to the Sunway iLabs team for their invaluable contribution and insights in preparing this article.

References

FutureX Insights. (2026). Robots Among Us. Futurexinsights.news. https://www.futurexinsights.news/p/robots-among-us?r=6i8ih6&utm_campaign=post&utm_medium=web

Morgan Stanley. (2026, January 26). Embodied AI and the Rise of Humanoid Robots. Morgan Stanley. https://www.morganstanley.com/

The Star Online. (2026). China’s humanoid robot makers pivot from “body” to “brain” as commercial race heats up. The Star. https://www.thestar.com.my/aseanplus/aseanplus-news/2026/02/07/chinas-humanoid-robot-makers-pivot-from-body-to-brain-as-commercial-race-heats-up

‌

‌

Sam Altman on Switching Sides of the Table

One of the most interesting parts of this recent a16z podcast conversation with Sam Altman wasn’t actually about Sora, AGI timelines, or even OpenAI’s next big product move.

It was about something far more human, and for many of us in the startup world, far more relatable: the transition from being an investor to becoming an operator.

As someone who has spent time on both sides of the table, I found myself reflecting on the same question Sam’s journey raises:

Am I better suited to investing in companies, or building them?

And perhaps more importantly:

What does it really take to make that switch?

From Investing to Operating: Different skills, different realities

Sam Altman is one of the most high-profile examples of someone who has successfully moved from investor to entrepreneur and CEO. But what this conversation highlights is how fundamentally different those roles can be.

Being an investor requires a particular set of muscles. Operating, however, is an entirely different experience.

Investors can step back. Operators cannot.

There is something relentless about being responsible not just for ideas, but for outcomes. The feedback loop becomes immediate, personal, and constant.

Sam also admits he was not particularly good at being a CEO at first, and that the role taught him humility. That honesty matters, because operating is not an extension of investing, it is a different discipline entirely, one you grow into through responsibility and repetition.

Why OpenAI Wasn’t a Typical Product Company Transition

Another fascinating point in the conversation is Sam’s view that moving into a research lab environment felt like a more natural evolution than running a pure product company.

That stood out to me.

OpenAI is not a traditional startup in the sense of optimising funnels or iterating on SaaS metrics. It sits somewhere between a frontier research institution, an infrastructure builder, and a consumer technology company.

Research labs reward a different kind of long-term thinking:

• Breakthroughs rather than incremental iteration

• Mission-driven talent density

• A longer horizon for impact

• Discovery as much as delivery

In that sense, OpenAI’s trajectory is less about building “a product” and more about building a platform for the future.

Sam’s transition makes sense in that context. The move from investor to operator may be difficult, but moving into a research-led organisation where the mission is as central as execution may feel more natural than jumping into a traditional product CEO role.

It reflects how entrepreneurship itself is evolving. Some of the most important companies today are not just selling software. They are building new technological frontiers.

A Personal Reflection: Enjoying Both Sides of the Table

This part of the podcast resonated with me because I’ve wrestled with the same question.

I’ve been an entrepreneur. I’ve been an investor. And I still ask myself what suits my skillset better: building companies or backing them.

The truth is, I genuinely enjoy both.

I find the act of investing intellectually exciting: identifying emerging technologies, meeting ambitious founders, and supporting companies at the edge of innovation.

But I also know that investing becomes far more grounded when you’ve experienced what it actually takes to run a company.

Operating teaches you things no pitch deck can:

• How hard hiring really is

• How messy execution becomes

• How difficult scaling culture can be

• How lonely decision-making feels at the top

That experience stays with you. And I believe it makes you a better investor, because you don’t just evaluate ideas, you understand the realities behind them.

The Best of Both Worlds: Investor, Intrapreneur… and Working with the Next Generation

What I love about my work today is that I get to combine multiple worlds that are often kept separate.

On one hand, I have the privilege of investing in next-generation technology companies shaping the future of AI, innovation, and entrepreneurship.

On the other hand, I also act as an intrapreneur: working within Sunway’s ecosystem to build and scale solutions that connect corporates, startups, academia, and government.

But what makes the work especially meaningful is that it doesn’t stop there.

I also get to spend time with students and researchers at Sunway University, engaging with the next ideas being developed in the lab, often at the earliest stage, before they are even companies or products.

There is something uniquely energising about being close to that frontier: watching curiosity turn into experimentation, and experimentation into innovation.

It is a reminder that entrepreneurship is not only about venture and execution, but also about learning, discovery, and the long-term cultivation of talent and ideas.

In many ways, this blend of investing, building, and working alongside emerging researchers feels like the most complete seat at the table.

It reinforces that the investor vs entrepreneur question doesn’t always need a binary answer.

Sometimes the most meaningful careers happen in the overlap, where you can support builders, while also building yourself, and helping shape what comes next.

Closing Thought

Sam Altman’s journey from investor to operator is a powerful example of how career paths in tech and entrepreneurship are rarely linear.

It also raises an important question for anyone in this space:

Are you energised more by identifying potential, or by executing against it?

And perhaps the best answer is that both perspectives, when combined, create a rare kind of leverage.

What about you?

Have you ever wrestled with whether you’re better suited to building companies or backing them? And do you think the best investors are those who have operated before?

REFERENCE:

In Star Wars robots weren’t special…they were useful.

Long before real-world robots showed up, sci-fi imagined a world where robots handled everyday work.

The arrival of robot vacuum cleaners in the early 2000s marked the dawn of service robotics. Designed to operate autonomously, they quickly gained traction in busy households, with early models like the Roomba selling over a million units within two years.

This opened up new possibilities for automation: service robots handling routine tasks around the clock without needing constant supervision.

Broadly speaking, this is what service robots are designed for, to work in everyday environments, helping organisations deal with labour shortages and long, repetitive tasks that humans would rather not do themselves, without getting tired.

They aren’t robots confined to factories or dealing with manufacturing products, but instead they are there to support daily tasks such as in hotels, hospitals, malls, airports and offices.

Beyond the Gimmick

For a long time, robots moved through the world by following fixed paths. They knew where to go, but not much about what they were looking at.

That has started to change.

Today’s robots often appear in demos, videos, and public showcases — from making appearances at shopping malls like Sunway Pyramid and i-City to dancing on shows like America’s Got Talent — capturing attention and showing what robots can do in motion.

With recent advances in AI, robots are increasingly able to make sense of spaces rather than just move through them. By combining vision, language, action (VLA) and large language model (LLM), today’s robots can interpret their surroundings and adjust how they move or work in real time.

You may already be familiar with some humanoid robots like Agibot, and robot dogs such as Unitree’s Go2 and Boston Dynamics’ Spot.

Still, robots aren’t just built to impress people, they’re there to do real work.

What These Robots Are Really Used For

Service robots have been taking on more practical roles from robot vacuums like iRobot’s Roomba cleaning floors at home to cat-eared waiter robots like Pudu Robotics’ BellaBot delivering your plate of roti canai at mamak restaurants.

So who else is already putting robots to work?

The Trouble with Keeping Things Clean

Cleaning is more complicated than it looks.

Primech AI’s Hytron robot is built for this exact challenge. It is designed to operate in tight, wet environments like restrooms, where automation is particularly difficult.

It can scrub floors, clean toilet bowls, and sanitise surfaces while navigating large facilities. This makes Hytron suitable for places where hygiene standards are high and cleaning needs to be consistent.

Hytron robots are already deployed in some of Singapore’s prominent public hospitals and shopping malls, and large facilities in parts of Asia and Europe, where cleanliness is non-negotiable.

Some other cleaning robots like Pudu Robotics’ CC1 and SoftBank Robotics’ Whiz are also designed for routine cleaning in commercial spaces.

The Trouble with Getting Things Delivered

In a hospital, things are constantly on the move.

Medications travel from pharmacies to wards, and meals, linens and lab supplies move between floors all day long.

Aethon’s robots such as the T3 and Zena RX can transport medical supplies securely, along scheduled routes across hospital floors, operating continuously throughout the day. By doing so, these robots can reduce the time nurses and staff spend moving items and free them to focus more on patient care. Relay and Ottonomy’s delivery robots also explore how supplies can be delivered with care across hospitals, hotels and other large facilities.

Aethon’s robots are already in use across major hospitals in the U.S and Singapore, including the largest fleet of over 90 robots deployed at Children’s Healthcare of Atlanta.

The Trouble with Parking

Parking is one of those problems that most people just put up with.

In dense cities, space can be limited, ramps are tight, and a lot of room is wasted just to accommodate human driving and door-opening.

Shenzhen Shanyi Technologies tackles this problem by removing the human driver from the parking process entirely. Their robots lift and move cars within dense parking facilities, slotting them into spaces that would usually be difficult, sometimes even impossible, to navigate manually. With its automated parking robot, cars can be parked faster and closer together.

Shenzhen Shanyi Technologies automated parking robots have moved beyond pilot trials into production, with systems adopted in commercial facilities in China, including large parking complexes, transport hubs and industrial sites.

Other automated parking systems like Parkie also focus on increasing space efficiency and reducing the hassle of parking.

Where This Leaves Us

By now you can probably picture the kinds of work robots are already taking on. Service robots aren’t meant to be shiny centerpieces, they’re built to do the work. There are many more service robots already operating and still being developed.

As technology matures and challenges around safety and deployment are worked through, we may find ourselves closer to the world of sci-fi…much like Star Wars, where robots live among us as helpers woven into everyday life.

Coming Up Next

Up next, we’ll zoom in to look at the robot value chain and how AI is reshaping robotics, from the brain that makes decisions to the body that carries them out.

Acknowledgements: Thank you to the Sunway iLabs team for their invaluable contribution and insights in preparing this article.

References:

International Federation of Robotics (IFR). (2025). Service Robots See Global Growth Boom. IFR International Federation of Robotics. https://ifr.org/ifr-press-releases/news/service-robots-see-global-growth-boom

Primech AI. (2025, June 12). The Real-World Use Cases of Robotics in Commercial Cleaning - Primech AI. https://primech.ai/the-real-world-use-cases-of-robotics-in-commercial-cleaning/

Robots Authority. (2024). The History of the Roomba: The First Commercially Successful Robot Vacuum. Your Guide AI and Robotics. https://robotsauthority.com/the-history-of-the-roomba-the-first-commercially-successful-robot-vacuum/

Shanyi Technologies（深圳閃移技術有限責任公司）৹ 最先端レーザー、ロボット、計測技術のカンタム・ウシカタ. (2024). Kantum.co.jp. https://www.kantum.co.jp/manufacturer/Shanyi

‌ST Engineering. (2025). Stengg.com. https://www.stengg.com/en/innovation/innovation-stories/transforming-hospital-operations-with-autonomous-mobile-robots/

‌

‌

‌

At first glance, AI feels like instant magic. Type a prompt, get an answer.

But when you look closer, things are not as simple as they seem. Behind every response sits a massive amount of computation, and it all starts with the brain.

In AI, the brain, also known as hardware accelerators, are specialised chips designed to handle the heavy mathematical operations behind AI training and performing inference. These chips process enormous amounts of data in parallel.

Graphics Processing Units (GPUs) dominate today’s AI ecosystem, while custom chips like Tensor Processing Units (TPUs) and Language Processing Units (LPUs) are built for more specialised AI tasks.

From School to the Workforce

A useful way to think of AI training is to compare it to how humans grow up.

As children, we spend years learning. We absorb knowledge, but only later does that learning begin to show its value, when we enter the workforce.

AI pretty much works the same way.

Before it can be useful, an AI model has to be trained. This training phase can take months and consumes enormous computing power (Epoch AI, 2025). Costs pile up, but there’s little direct payoff at this stage. Like education, training is expensive…but necessary.

Once an AI model has gone through training, the real question is this:

How does it deliver answers in real time—quickly and reliably?

Because AI chips don’t just train models, they’re also responsible for inference—the process of generating answers from prompts. This depends on what’s happening inside the AI’s brain at the moment the request comes in. So let’s break down how GPUs retrieve and process data during inference.

GPU, TPU and LPU: Where is Data Stored?

Picture AI running inference like chefs busy preparing cheesecakes for new orders. The bakery kitchen represents the GPU, while high-bandwidth memory (HBM) is the large storeroom next door where ingredients are stored and retrieved.

• The storeroom = HBM, where ingredients (data) are kept

• The counters, shelves, and clear walkways = bandwidth, allowing ingredients to move quickly and in bulk

• The bakery kitchen = the GPU, where all the baking (inference) happens

• The chefs and baking stations = the cores that do the work

The chefs first fetch the ingredients they need from the storeroom, carry them across the kitchen, and bring them to their stations, where the actual baking happens.

They can prepare many cheesecakes at the same time, as long as ingredients keep flowing smoothly from the storeroom to the chefs. This setup isn’t unique to GPUs. Other hardware accelerators, such as TPUs, also rely heavily on fetching data from HBM.

But here’s the catch.

When too many chefs are baking at once, they often need the same ingredients simultaneously. Even with wide walkways and counters, chefs start waiting on one another. As orders pile up, movement slows, bottlenecks form, and the kitchen becomes congested.

In short, GPUs rely on a large, shared storeroom that many chefs must access at once. This is why GPUs can experience heavy memory traffic and higher inference latency when generating responses.

But relying on a shared storeroom (HBM) isn’t the only way to prepare an order.

LPU: Ingredients at the Baking Station

Now imagine a different kitchen.

Instead of chefs constantly walking to a shared storeroom, each baking station has its own small shelf of ingredients. This shelf represents SRAM, much smaller than the main storeroom (HBM), but placed right where baking happens.

Because the ingredients are already within arm’s reach, there’s no waiting, no queueing, and no kitchen congestion. Chefs can start baking immediately, resulting in faster and more predictable response times.

This approach has delivered around 2× faster real-time inference in independent tests, which helps explain why LPUs are attracting attention as a complement to GPUs (CNBC, 2025).

The trade-off is capacity.

Each station’s shelf can only hold a limited number of ingredients, so it can’t replace a large shared storeroom. Fitting much more of this fast storage directly into the kitchen remains a major technical challenge (Asia Business Daily, 2026).

Also, LPUs are designed mainly for inference. Training large AI models still depend on GPUs, TPUs and other training-focused hardware.

The Brain Now Needs a Traffic Controller?

As we know by now, AI runs on massive computing power, AI chips aren’t getting any cheaper, supply is tight, and no existing architecture fits every job. As more specialised chips enter the mix, new questions emerge:

How do you decide which chips to use (US or China), how do you get the most out of the chips you already have and what if you want to use chips from both US and China?

Something has to manage the flow of work between them. This is where software steps in.

These traffic controllers decide which jobs run on which chips and when. The goal is to keep expensive AI chips busy doing useful work rather than sitting idle or waiting in line. In practice, this means spreading workloads more evenly and making sure each chip is used for what it does best.

This is where platforms like Infinigence, Exostellar, Run:ai and Flex:AI come in:

Infinigence and Exostellar try to solve a broader problem. Both focus on optimising and coordinating across different chip types from different vendors within the same platform.

Infinigence supports both U.S. and China AI chips, while Exostellar focuses on U.S-based GPU ecosystems.

Meanwhile, Run:ai and Flex:AI, focus on optimising AI chips within a specific hardware ecosystem. Run:ai works best with NVIDIA chips, ensuring that all GPUs are used as efficiently as possible, whereas Flex:AI, developed by Huawei, does a similar job but for Huawei’s Ascend chips and other China-based AI chips.

Where This Leaves Us

AI has moved beyond its early days. The focus is shifting from training models to making inference faster, cheaper and more reliable. Much like the internet, progress was slow and access was expensive, but over time, costs fell and usage scaled. In the next phase of AI, efficiency won’t be a nice-to-have, it will be a competitive advantage.

Coming Up Next

Now that we’ve unpacked the brain behind AI, we turn to where the brain is deployed. We’ll explore more on physical AI — where AI systems are embedded into machines that see, move and act in the real world.

Acknowledgements

Thank you to the Sunway iLabs team for their invaluable contribution and insights in preparing this article.

References

Asia Business Daily (2026). Welcome To Zscaler Directory Authentication. Asiae.co.kr. https://cm.asiae.co.kr/en/article/2025123016015748006

Emberson, L. (2025). Frontier training runs will likely stop getting longer by around 2027. Epoch AI. https://epoch.ai/data-insights/longest-training-run

‌Faber, D. (2025). Nvidia buying AI chip startup Groq’s assets for about $20 billion in its largest deal on record. CNBC. https://www.cnbc.com/2025/12/24/nvidia-buying-ai-chip-startup-groq-for-about-20-billion-biggest-deal.html

FutureX Insights. (2026). Smartphones are Getting Pricier Because of AI?. https://www.futurexinsights.news/p/smartphones-are-getting-pricier-because

‌Laurent, A. (2025). Nvidia’s $20B Groq Deal: Strategy, LPU Tech & Antitrust. IntuitionLabs. https://intuitionlabs.ai/articles/nvidia-groq-ai-inference-deal

‌

For corporates, this allows for efficiency, reducing turnaround time, and making better use of legal expertise across the organisation.

What Is Legal Tech?

Legal tech refers to software and AI-based systems designed to help legal professionals work more efficiently by handling routine tasks such as research, drafting, contract review, due diligence, compliance checks, and post-execution management, while humans remain in control of all legal decisions.

How Legal Tech Adds Professional Value in a Corporate Setting

For corporates, the value of legal tech lies in its ability to support legal teams handle large and complex workloads with consistency. AI-enabled tools can review contracts and legal materials at scale, applying the same standards set by legal teams across documents, which can be useful for companies dealing with recurring agreements, regulations, or operations.

Legal tech saves time by taking on repetitive, low-value tasks that requires days or even weeks, such as initial contract reviews or information extraction. This frees internal legal teams and external legal advisors to focus more on important work such as analysis, risk assessment, negotiation, and strategic advice that directly support business decision-making.

Just as importantly, some legal tech tools are designed to spot potential risks areas by flagging unusual clauses, key obligations, or deviations from standard terms. When used appropriately, this can support quality control and help reduce the likelihood of issues being overlooked due to workload volume.

Key Legal Tech Tools and Their Roles

Choosing the Right Legal Tech

For corporates, adopting legal tech works best when it starts with everyday realities rather than technology itself. A helpful first step: Identify where legal processes most often slow the business down, whether that is waiting for contract reviews, multiple drafting iterations, or time spent clarifying legal positions.

Matching Tools in Practice

Different legal AI tools are designed for various types of legal work. Solutions such as Harvey, CoCounsel, or Lexis+ AI can help legal teams and external legal advisors work more efficiently on research and drafting.

At the same time, platforms like Luminance or Libra are better suited for handling high volumes of contracts and compliance-related reviews, such as tenancy agreement, MOUs, Non-Disclosure Agreement (NDA), as well as contract comparison, particularly when benchmarking documents against a consistent standard set by the legal team.

Most importantly, successful adoption respects how legal teams work. Tools that fit naturally into familiar environments, such as Microsoft Word or existing document systems, are far more likely to be used effectively.

A Real-World Perspective

That shift is already visible in practice. A Fortune article published in May 2025 highlights how AI-powered legal tools such as Harvey are taking on routine, time-insensitive tasks, allowing junior lawyers to engage earlier in more analytical and problem-solving work.

More broadly, this reflects how AI can change the balance of work within teams, creating more exposure to practical, real-world experience, while routine tasks are handled more efficiently in the background.

Major Industry Signals: AI Investment and Strategic Acquisitions

Harvey’s $8 Billion Valuation Backed by Andreessen Horowitz

Andreessen Horowitz (a16z) a venture capital firm behind Airbnb, Facebook, Figma, and GitHub, has taken major step into legal tech by leading a $150 million funding round into Harvey, valuing the company at around $8 billion (Bloomberg, 2025). This investment suggests growing market validation that AI for legal workflows is becoming core infrastructure for modern legal practice.

Wolters Kluwer Acquires Libra, Strengthening Integrated AI for Legal

Another significant development came from Wolters Kluwer’s — a European-based professional information and software company — acquisition of Libra Technology GmbH for up to approximately €90 million (RTTNews, 2025). The move integrates Libra’s AI legal assistant capabilities with Wolters Kluwer’s extensive legal content and tools, aiming to deliver a unified platform for legal research, drafting, and analysis built to support lawyer workflows.

Together, these developments show that legal tech is not a trend, but an evolving ecosystem shaped by market demand and professional standards.

Legal Tech Is an Enabler, Not a Replacement

Legal tech does not diminish the role of lawyers, it amplifies professional expertise. These tools help legal professionals focus on what humans do best; judgment, interpretation, strategy, and client care.

The latest investments and acquisitions, from Harvey’s billion-dollar valuation boost to Wolters Kluwer’s integration of Libra, reflect confidence that legal tech will partner with legal professionals to elevate practice standards, not undercut them.

Legal tech is not a shortcut. It is a strategic ally that strengthens how legal work is done, how insights are generated, and how clients are served.

Disclaimer: This article is for general information only and does not constitute legal advice.

References

Clark, K. (2025). Andreessen Horowitz Invests in Legal AI Startup Harvey at an $8 Billion Valuation. Bloomberg. https://www.bloomberg.com/news/articles/2025-10-29/andreessen-horowitz-invests-in-legal-ai-startup-harvey-at-an-8-billion-valuation

Mollman, S. (2025). An AI tool has become so useful to junior lawyers at PwC that “there’d be a riot” if the firm took it away, says partner. Fortune. https://fortune.com/2025/05/09/ai-legal-tool-harvey-junior-lawyers-careers/

‌RTTNews. (2025). Wolters Kluwer To Acquire Libra Technology For Up To EUR 90 Mln. Nasdaq.com. https://www.nasdaq.com/articles/wolters-kluwer-acquire-libra-technology-eur-90-mln

‌

Recently, word has spread that the AI boom is driving a severe shortage of memory chips, the same components used in both AI data centres and everyday devices like smartphones.

SK Hynix and Samsung, two of the world’s leading suppliers of memory chips, have already sold out their high bandwidth memory (HBM) supply through 2026, with industry expectations that the memory shortfall could stretch into late 2027 (Reuters, 2025). As supply tightens while demand remains red-hot, prices rise. Real inflation pressure.

If artificial intelligence (AI) demand is strong enough to push up prices in everyday tech such as phones and laptops, it tells us something important, that our ChatGPT subscription isn’t getting cheaper anytime soon.

What’s really keeping AI expensive?

To make sense of it, the Sunway iLabs team stepped back and compared notes, and one thing became clear. This isn’t a single-issue problem. AI needs three main things to work at scale, and all three are under strain. To paint the picture, AI is intelligence with dependencies: it needs a body to carry it, a brain to think and fuel to run it.

Infrastructure: The body that holds everything together

Infrastructure is the body of AI, the physical foundation everything else plugs into. This includes data centres, and those data centres depend on a whole chain beneath them, such as grid connections that deliver electricity, transformers that manage it, and power plants that generate it in the first place. AI chips can’t run without buildings to house them.

One reason AI isn’t getting cheaper is the sheer cost of building this body. Big tech companies are spending hundreds of billions of dollars building AI-ready data centres, so much so that data centre construction is set to overtake office buildings by 2026 (The Wall Street Journal, 2025c). These aren’t ordinary buildings, they require specialised cooling, power distribution, and built-in backups, all of which drive costs higher.

And then there’s time. Even with unlimited funding, data centres and grid connections can’t be built overnight. Physical infrastructure expands slowly, in years rather than months. That heavy, long-build reality puts a natural limit on how fast AI can scale, and helps explain why costs stay high, even as the AI technology itself continues to improve.

Hardware accelerators: The brain doing the thinking

If infrastructure is the body, then hardware accelerators are the brain. AI semiconductors like GPUs and TPUs do the heavy mathematical work behind training and running AI models.

The challenge is that this brain is hard to scale. Most of the world’s AI chips, such as GPUs and TPUs are manufactured by a single company, TSMC. As demand has surged, supply hasn’t been able to keep up, making shortages and pricing pressure hard to avoid.

A major bottleneck isn't just making the chips themselves, but packaging them. AI chips require complex packaging processes that depend on specialised equipment from many vendors and expanding this capacity takes time (Epoch AI, 2024). And because NVIDIA's GPU chips have become the industry standard, many companies are effectively competing for the same limited supply.

Alternatives like Google's TPUs are either tightly controlled, while others such as Huawei's Ascend chips are still catching up (The Wall Street Journal, 2025a). So even as AI software advances quickly, the brain it runs on doesn't scale at the same speed, which helps explain why costs are unlikely to fall meaningfully anytime soon.

Power: The fuel that keeps the lights on

AI doesn’t just think, it consumes. Training AI models takes enormous amounts of electricity, and once those models are deployed, every question asked, image generated, or chatbot reply — known as “inference” — adds to the load. Together, training and usage keep the power meter running almost constantly.

This is where costs start to balloon. Some countries are better positioned than others. In this case, China has a relative edge, with far greater power-generation capacity than the US and cheaper electricity in many regions (The Wall Street Journal, 2025b). The US, by contrast, has strong demand but slower grid expansion, creating congestion and rising costs (Epoch AI, 2024).

At the same time, emerging, fast-growing markets like Malaysia are accelerating data centre expansion precisely because electricity costs are significantly lower than in the US (ACCEPT, 2025).

But here’s the catch: cheaper power doesn’t solve everything. AI remains extremely energy-hungry. Unless AI systems become far more energy efficient, electricity costs alone will keep AI expensive, even in energy-rich countries.

Where this leaves us

When you line up the clues, things start to make sense. AI isn’t expensive just because innovation has stalled. It’s expensive because multiple parts of the system are under strain at the same time.

The most visible constraints are physical: a body that takes years to build, a brain that’s hard to scale, and fuel that keeps burning. Together, they help explain why AI costs remain high. But the physical constraints aren’t the whole story, software, operations and geopolitical conditions matter too.

We’ve seen this pattern before. In the early days of the internet, access was limited, hardware was pricey, and bandwidth felt like a luxury. Over time, as networks expanded and technology matured, costs came down and the internet became accessible everywhere.

AI may follow the same path. But for now, this isn’t a one-bottleneck problem. It’s a system problem.

Coming Up Next

In the upcoming piece, we’ll zoom in on hardware accelerators, the brains behind AI. We’ll take a a closer look at GPUs, TPUs and emerging alternatives like LPUs, explore how these chips differ, where each excels and what their evolution could mean for the future scalability of AI.

Acknowledgements

Thank you to the Sunway iLabs team for their invaluable contribution and insights in preparing this article.

References

• Huang, R., & Qin, S. (2025a). Huawei Unveils AI Chip Pipeline as Tech Rivalry Heats Up. The Wall Street Journal. https://www.wsj.com/tech/huawei-unveils-ai-chip-pipeline-as-tech-rivalry-heats-up-07600dc8?mod=article_inline

• Huang, R. & Spegele, B. (2025b) China’s AI Power Play: Cheap Electricity From World’s Biggest Grid. The Wall Street Journal. https://www.wsj.com/tech/china-ai-electricity-data-centers-d2a86935?st=6h2Mq9&reflink=desktopwe…

• Jin, H., Potkin, F., Lee, W.-Y., Bridge, A., & Cherney, M. A. (2025). The AI frenzy is driving a memory chip supply crisis. Reuters. https://www.reuters.com/world/china/ai-frenzy-is-driving-new-global-supply-chain-crisis-2025-12-03/

• Maurer, M. (2025c). AI Construction Costs Can Be an Accounting “Black Box.” The Wall Street Journal. https://www.wsj.com/articles/ai-construction-costs-can-be-an-accounting-black-box-3c197b09

• Nathania Azalia. (2025). The Rise of Data Centres, Artificial Intelligence, and ASEAN’s Decarbonisation Goal - ASEAN Climate Change and Energy Project (ACCEPT). ASEAN Climate Change and Energy Project (ACCEPT). https://accept.aseanenergy.org/the-rise-of-data-centres-artificial-intelligence-and-aseans-decarbonisation-goal

• ‌Sevilla, J. (2024). Can AI Scaling Continue Through 2030? Epoch AI. https://epoch.ai/blog/can-ai-scaling-continue-through-2030

If you told a toy executive 15 years ago that a Chinese collectible brand would be worth more than Hasbro, Mattel and Sanrio combined, they’d probably laugh you out. Yet as of mid-2025, Pop Mart’s valuation stood at roughly US$44.43 billion, surpassing the old-school giants of the global toy industry.

The secret isn’t plastic. It’s feelings.

Gen Z and the Rise of Emotional Value

And now, increasingly, Gen Z consumers are shopping for emotional return on investment (including some GenX and Y, ahem...). Blind box toys, where buyers don’t know which character they’ll get, perfectly tap into this mindset. The thrill of uncertainty, the dopamine hit of surprise, and the comfort of collecting, all play into a deeper desire, emotional regulation.

Characters like Labubu, Molly, Skullpanda, and Crybaby aren’t just toys. They’re mood avatars. One Skullpanda for a melancholic day. A Crybaby is clipped onto a bag when life feels overwhelming. A Labubu guarding your phone like a mischievous emotional support gremlin. These figurines travel with their owners as accessories, tiny reflections of inner states.

Then the blind box concept hasn’t stayed in toys. It’s quietly resurfaced in entertainment—especially music. Buying albums with randomised celebrity photo cards, forcing fans to purchase multiple copies to get the idol they want, feels curiously like the 1990s all over again: same psychology, new packaging.

Buying something with “little practical value” suddenly makes perfect sense when it contributes heavily to emotional well-being.

But emotional economies are fragile. By December 2025, Pop Mart’s stock had slid nearly 44% in just four months, wiping out over US$25 billion in market value. One primary concern: overexposure. Analysts warned that aggressive production, especially of Labubu, could destroy the scarcity that made the brand desirable in the first place.

When everyone has the same emotional anchor, it stops feeling special.

What’s the next emotional investment after blind boxes?

Enter CES 2026, the Consumer Electronics Show — the world’s biggest stage for consumer technology. This year, one signal became impossible to ignore: physical IP is converging with AI companionship. It’s not so much about the clunky, humanoid robots anymore but shift toward something far cuter — a plushie that interacts with you and… talks to you (yes, slightly Chunky vibes).

• Ropet by Mengyou Intelligence first appeared at CES 2025. Pre-sold in JD.com China, and covered by Forbes, BBC, and CNBC, it is emotionally responsive. Then, more in CES 2026, the floodgates opened.

• Leimeng Technology’s Bibo looks like an electronic cousin of Labubu. It recognises faces, tone, and movement, reads emotional states, and proactively interacts using emotional AI.

• Lovipeer by LOVEAXI goes deeper, combining micro-expression recognition, NLP, adaptive learning, and bilingual conversation. It can comfort you when you’re sad, and tutor your child in English when you’re not.

• Honghuo Moya, wrapped in plush fur, focuses on sleep and “active embodied empathy,” offering emotional support alongside practical assistant functions.

• AiMOON by Lynxaura Intelligence (灵犀智能), which looks similar to skullpanda, is powered by zodiac culture and emotional memory systems. It tracks moods, remembers preferences, and shifts personality accordingly: therapist, hype partner, or professional complainer.

Even legacy giants are paying attention. Panasonic’s NICOBO responds to touch, recognises faces and voices, and exists purely to make homes feel less lonely.

Where This Leaves Us

Surprise boxes were never really about toys. They were about outsourcing emotion, letting objects carry feelings we didn’t know how to process ourselves. AI companions take this idea one step further. Instead of silent figurines, we now have responsive, learning, emotionally aware entities.

In the future, every household will likely own one or two companion robots. Not as gadgets, but as emotional infrastructure, perhaps, digital extensions of the human.

But one thing is clear, the next wave of consumer tech won’t just solve problems. It will feel something while doing it, and maybe think with us. Cute or creepy? We’ll find out soon, won’t we.

Across Southeast Asia, the startup ecosystem is entering a tougher funding environment. Recent high-profile startup failures have reinforced a more cautious approach to funding, bringing recurring red flags into sharper focus, including poor governance, inflated revenues, and unsustainable burn rates.

This current retreat is a much-needed correction that is also reshaping what investors look for next. Investors are increasingly backing start-ups with strong intellectual properties and differentiated artificial intelligence (AI) capabilities compared to those focused solely on operational execution, says Karen Lau, chief operations officer of Sunway Innovation Labs (Sunway iLabs), the innovation lab and corporate venture capital (CVC) arm of Sunway Group.

The Edge — December 29, 2025
Read More...

Sunway iLabs and and JETRO concluded the J-StarX Malaysia Deep Tech Programme (MYTech) 2025, accelerating four Japanese startups’ expansion into Malaysia and the Asean region. Marking its seventh year of collaboration, the accelerator programme leveraged on Sunway’s living laboratory for market access and JETRO’s extensive network, reinforcing Malaysia as a launchpad for Japanese innovators seeking regional growth opportunities.

The 2025 cohort featured Japan’s diverse applications of AI and deep tech including Buddy Cloud’s mobile-enabled veterinary diagnostics, FiberCraze’s sustainable nanofibre textiles, BioPhenolics’ bio-based phenolic compounds, and I.W.G’s interoperable healthcare data platform, DoCloud — each reflecting how innovation can create meaningful, sustainble impact across industries.

Digital News ASIA — November 27, 2025
Read More...

FutureX Ventures Fest redefines the traditional conference:

curating an exclusive AI / deep tech beachside gathering of 200 selected leaders from startups, investors, government, and corporates to spark meaningful conversations shaping the future of AI / deep tech while strengthening Malaysia’s position as a regional innovation hub.

Future of AI / Deep Tech Driving Real-World Impact

This year, FutureX is anchored around three key themes:

1. Reimagining Healthcare with AI

2. AI Frontier & Emerging Breakthroughs

3. AI as a Catalyst for Ecosystem Growth

With perspectives from leading players like a16z Perennial, one of the most successful tech investors in the world; OpenAI, the company behind the world-renowned frontier model, ChatGPT; and thought leaders from global investment and innovation ecosystems, the conversations go beyond hype into what it truly takes to build, fund, and scale the next wave of technology.

Good Vibes To Expect

Set against a beachside backdrop, FutureX Ventures Fest blends intellectual exchange with experiential moments, from the Sands of Surprise and Networking Soiree to Fire Show and DJ Soundwave Session, creating a dynamic setting where meaningful connections happen both on and off stage.

Join us at FutureX Venture Fest 2026 and be part of the conversations shaping what’s next: let’s make waves together.

🌊 REGISTER NOW🌊

Wondering what FutureX is all about? Catch a glimpse at last year’s highlights:
Watch video...

The FutureX Connect event brought together cross-border startups and visionary leaders from Malaysia, Japan, South Korea, China and beyond to dive deep into the world of AI and deep tech 🌐

At the 22 October 2025 event, leading minds in AI and Deep Tech unite with one mission: to foster cross-border innovation that pushes boundaries and transforms industries.

Throughout the event, industry leaders and experts got the chance to move beyond showcasing innovation and into real connection. Founders, corporates, and ecosystem builders came together to question, collaborate, and co-create — turning global perspectives and local ambition into meaningful momentum.

Watch Video...

That momentum carried into broader industry conversations. At the panel “From Borders to Breakthroughs: AI’s Role in Global Industry Transformation,” ecosystem stakeholders and industry experts shared how early AI adoption is already seeing measurable impact. As Malaysia advances its NIMP 2030 vision, the discussion highlighted the need for stronger public-private collaboration, smarter factories, and a workforce ready to adapt and innovate.

Read More...

🌊 Making Waves in AI / Deep Tech 🌊

FutureX Ventures Fest, a high-energy beachside convergence of AI and deep tech minds brings together visionary founders, forward-thinking investors, and ecosystem builders to exchange ideas on trends, opportunities, and what it takes to build successful early-stage deep tech startups.

It is a space where people are showing up for:
💧Connections that matter
💧Insights that spark breakthroughs
💧To walk the AI & Deep Tech talk while having the time of your life

At the 2025 edition of FutureX Ventures Fest, held on 15 April 2025, the energy, conversations and connections spoke for themselves. Somewhere along the way, it earned the nickname “the Coachella of investors and startups” and we’re not arguing.

Missed it? Drop us a message to catch the next one.
Watch video...

Remember when ChatGPT rolled out its GPT-4o image generator earlier this year and suddenly half the internet became ghiblified? Social media feeds were flooded with AI-generated images that looked straight out of a Studio Ghibli film, from celebrities and politicians to movie scenes, memes, and even people’s pets.

If AI can imitate creative works so convincingly that it feels almost… too good, where do we draw the line between inspiration and imitation — and who owns it?

Read More...