In the second week of August 2009, four months into my first job as an R&D engineer at a pulp and paper plant in Riau, I told my supervisor I was leaving to join a bank. I was 23. I had spent four years studying chemical engineering, graduated at the top of my department, and worked my way into the bleaching process team at one of the country’s largest paper producers. I had also concluded that what I wanted to do for the next four decades was sit on the other side of how risk moves through systems, and that finance was where that work lived.

My supervisor told me, more politely than I deserved, that I was making a mistake.

I am now 15 years into a risk career that has run through three banks, an insurer, and eventually my own company. In every one of those seats, the skills I have leaned on hardest were not the ones I picked up in business school or in risk certification cycles. They were the ones I learned in a chemical engineering lab in North Sumatra.

This is not a coincidence. The principles that govern how chemical plants work happen to be the same principles that govern how financial systems work. The finance industry, by and large, has not noticed.

What I did not know I was learning

The chemical engineering curriculum I went through at Universitas Sumatera Utara was, on the surface, about reactors, separation processes, mass transfer, and process control. What it actually trained was a way of seeing systems.

The discipline forces you to think in terms of inputs, outputs, transformations, and constraints. Nothing inside a chemical process is allowed to be vague. If you do not know where a stream goes, you do not understand the plant. If you cannot predict how the system will behave when one variable changes, you cannot operate it safely. The mental habit this builds, the refusal to leave any part of a system unaccounted for, turns out to be the same habit you need to run a risk function inside a bank.

Also Read: Southeast Asia’s fintech apps don’t have a literacy problem, they have a fear problem​

Three principles that transferred

Three things in particular have stayed with me, and have shaped every risk decision I have made since.

Mass balance. In a chemical process, every kilogram of material in must equal every kilogram out, less what is accumulated or transformed inside the system. There are no unexplained gaps. The reflex this builds, that an unbalanced ledger is a mis-measurement, not a mystery, is the same reflex that catches operational losses, fraud patterns, and capital gaps inside a bank. A risk officer who instinctively believes that what comes out of a system must equal what went in, less what was retained, asks the right questions almost without thinking.

Process control and feedback. In a well-designed chemical process, the system measures itself continuously and adjusts in real time. The temperature drifts above the setpoint, a valve closes. The pressure spikes, a relief opens. There is no quarterly review committee. The system corrects, or it ruptures. This way of thinking about feedback, small adjustments made continuously against a known boundary, is exactly what most financial risk frameworks still do not do. They review periodically. They escalate sporadically. They operate, in engineering terms, like a reactor with no instrumentation.

Failure mode and effects analysis. Before any chemical plant goes live, the engineering team systematically maps every way the process can fail, ranks the failure modes by likelihood and consequence, and designs the controls before the failure happens. The discipline of asking “what would have to be true for this to go badly?”, not as anxiety, but as method, is the single most useful habit I brought into risk management. Most of the loss events I have seen across two decades were predictable inside a competent failure analysis. Most of them did not have one.

Where the finance training falls short

Finance has its own analytical apparatus. Modigliani-Miller, Black-Scholes, value-at-risk, the architecture of modern asset pricing. These are powerful tools inside the assumptions they were built for. Outside those assumptions, they are quieter than their reputations suggest.

The gap I have noticed, across many years of working with both engineers who entered finance and finance professionals who stayed in finance, is this. The engineer asks first: what could break this system, and what would be true if it did? The finance professional asks first: what is the expected outcome, and what is the variance around it? Both questions matter. But in a crisis, and risk management is the discipline of crises, the engineer’s question is the one that saves the institution.

Also Read: How do you finance a first nuclear reactor for a data centre? The deal structure is finally coming together

What this means for anyone choosing a non-traditional path

I get asked, perhaps once a month, by an engineering student whether they should leave technical work for finance, strategy, or consulting. My answer has become consistent.

The pivot is not the question. The skills you have built in engineering are some of the most transferable skills any discipline produces. You can move into finance, and your engineering training will quietly do half the work of your new role. The question is whether you are leaving for the right reason, because the systems you want to understand are now financial systems, not chemical ones, or because you think the new field will be more glamorous than the one you trained in.

If it is the first reason, the move is sound. If it is the second, no field will deliver what you are hoping for.

I do not draw flowsheets on whiteboards. I do not solve heat transfer equations. But the way I think about risk, about systems, balances, feedbacks, failures, was shaped by four years at USU and four months in Riau before I ever opened a banking textbook. 15 years later, that training is still doing more of the work than anything I learned afterwards.

The most useful risk management education I have ever received was a chemical engineering degree. It just took me a decade to fully realise it.

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A great deal of bad strategy begins with a comforting number.

Market share is one of the most over-trusted measures in business because it looks like proof of strength while often revealing very little about actual control. It tells you how much of the market you currently touch. It does not tell you who sets the terms, who shapes behaviour, who captures the best economics, who sees demand first, who becomes hard to route around, or who gets stronger when everyone else grows.

That distinction matters more than most leadership teams admit.

A company can have impressive market share and still be structurally weak. It may be large but replaceable. It may serve many customers without controlling any decisive part of the system. It may be visible in the market but absent from the points where pricing power, dependency, switching cost, regulatory comfort, workflow design, or standards actually get determined. In that situation, share creates exposure more than power. The company has more revenue to defend, more cost to carry, and more surface area to lose.

Power comes from something else.

Power comes from control points.

By control points, I mean the parts of a market that others must pass through, design around, conform to, or receive permission from. These are the places where choice narrows, dependence increases, economics concentrate, and leverage becomes durable. Control points are not always the biggest part of the value chain. In many markets they are the smallest visible layer and the most important strategic position.

Market share measures presence, control points determine terms

This is the first distinction serious strategists need to make.

Market share answers the question, how much of the market do we currently serve? Control points answer a much more important question: under what conditions does the market operate, and how much influence do we have over those conditions?

That is a harder question because it forces leaders to examine where actual leverage sits. Does the company control distribution? Does it control customer identity? Does it control switching friction? Does it control access to demand? Does it control compliance interpretation? Does it control the data that trains the system, validates performance, or proves value? Does it control the workflow where alternatives become painful? Does it control the commercial mechanism through which everyone else gets paid?

These are very different positions from simply being widely used.

Also Read: AI-powered business automation: How SMEs are transforming operations in Southeast Asia

The real contest in markets is usually over choke points, not customers

We often describe competition as a fight for customers, but that is usually only the surface-level view. Underneath that visible contest is another one. Companies are competing to own the choke points that shape how customers are acquired, how products are integrated, how risk is managed, how spending is justified, and how alternatives are compared.

This is where strategic thinking gets more interesting.

A control point may sit in onboarding, where identity and trust are established. It may sit in the workflow, where staff do not want to relearn behaviour. It may sit in the reporting layer, where leadership sees value and performance. It may sit in compliance, where approval becomes easier for one route than another. It may sit in the commercial structure, where procurement can buy one thing cleanly but struggles to buy the alternative. It may sit in data custody, where the history required for tuning, insight, and continuity quietly accumulates in one place.

None of these is glamorous in the way market share is glamorous. But they are often far more consequential.

Control points are often hidden inside boring functions

One reason leaders miss control points is that they expect power to sit in obvious places. They look for power in brand visibility, revenue scale, installed base, or category leadership. They do not always notice that durable influence is often buried in functions that appear mundane.

Billing can be a control point. Identity can be a control point. Audit records can be a control point. Procurement approval paths can be a control point. Data lineage can be a control point. Technical certification can be a control point. Distribution rights can be a control point. Default settings can be a control point. Even complaint handling can become a control point if it determines who the institution trusts when something goes wrong.

These positions rarely get celebrated in market narratives because they are not as exciting as product innovation or growth curves. Yet they are often where strategic reality lives.

A company that owns a boring control point can quietly become impossible to displace. Everyone else may appear more dynamic, more loved, or more talked about. But when the market has to choose under pressure, the firm sitting inside the operational necessity tends to win.

Also Read: Why AI-empowered teams are getting smaller, and why that is harder than it sounds

The most valuable control point is often the one that feels legitimate

Not every choke point becomes durable power. Some create resistance, regulatory backlash, or market workarounds. The most defensible control points are usually the ones that feel justified by the system rather than artificially imposed on it.

This matters a great deal.

A control point lasts when participants accept that it serves a real function. It reduces uncertainty. It simplifies coordination. It lowers risk. It improves trust. It makes the system easier to govern. It creates a common language for decision-making. It becomes part of how the market keeps itself stable.

This is why legitimacy matters more than mere friction.

An artificial barrier can generate temporary leverage, but a legitimate control point generates embedded authority. Participants may not love it, but they recognise that the market works better with it than without it. Once that happens, the control point stops feeling like an advantage and starts feeling like infrastructure.

That is when strategy becomes hard to attack.

Strategy is not only about getting chosen, it is about becoming hard to route around

That is the deeper idea underneath this whole argument.

Many strategies are built around being selected again and again. That is fine in open competition, but it is exhausting and fragile if every decision resets the contest from the beginning. Truly strong strategic positions do something else. They reduce the frequency with which choice is genuinely reopened.

This does not always mean lock-in in the crude sense. It can mean being embedded in the reporting layer where value is measured. It can mean being the trusted source of operational truth. It can mean becoming the easiest path through governance. It can mean owning the transition cost. It can mean sitting where multiple parties coordinate. It can mean controlling the evidence required to compare alternatives fairly. It can mean becoming the familiar answer in moments of uncertainty.

These are all forms of route control.

Once a firm occupies that place, competitors may still exist, customers may still express dissatisfaction, and market share may still move at the edges. But the company remains difficult to route around because it has become part of the operating logic of the system itself.

That is much closer to power than popularity ever is.

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Southeast Asia sits at an inflection point. The region commands 10 per cent of global GDP, controls critical supply chains, and possesses over 500 million people, a workforce larger than the European Union. Governments from Singapore to Japan have committed tens of billions to AI development. Training programmes have created millions of “AI-skilled” professionals. Investment is flowing. Ambition is high.

Yet the region remains a consumer of AI technology rather than a builder of AI leadership.

The gap isn’t ambition. It’s infrastructure. Specifically, the infrastructure to identify, verify, and rapidly deploy genuine AI capability at scale. Southeast Asia has solved the training problem. It hasn’t solved the capability verification problem. And that distinction is costing the region billions in unrealised opportunity.

The paradox: Trained workforce, unverified capability

Consider what Southeast Asia has accomplished in workforce development. Singapore alone has trained over 555,000 workers through SkillsFuture programmes. Indonesia, Vietnam, and other regional economies have launched comparable initiatives. Japan’s government has committed to ¥10 trillion (US$63.4 billion) in Trustworthy AI investment by 2030, explicitly supporting workforce development. South Korea, Taiwan, and the broader APAC region are following similar trajectories.

The scale is impressive. The investment is real. The problem is also real: credentials don’t predict capability.

Research is unambiguous on this point. Cloud Range’s 2025 analysis of technical workforce readiness found: “Knowledge is what you learn. Readiness is what you can perform, and those are not the same. In a live incident, the difference between knowing what to do and being able to execute in real time under uncertainty is dramatic.”

Google discovered this through hiring at scale. After years of screening candidates using transcripts, GPAs, and certifications, the company concluded these credentials were essentially “worthless” for predicting actual job performance. Only 43 per cent of people in STEM roles even have STEM degrees, yet these roles are filled regardless.

In Southeast Asia, the signal integrity problem is regional. Hiring challenges across the region are driven by “skills specificity rather than qualification mismatches,” meaning organisations aren’t struggling to find credentialed people; they’re struggling to find people with demonstrated expertise in the specific capability required. This distinction, credential versus demonstrable capability, is the invisible ceiling on regional AI leadership.

Also Read: The localisation gap: Why multilingual AI isn’t enough for APAC markets

The infrastructure gap: Why Southeast Asia can’t deploy capability

Three infrastructure deficits are blocking Southeast Asia’s path to AI regional leadership.

First, no reliable capability signals. When Singapore employers report that 24.3 per cent experience skills gaps, and 49.9 per cent say this causes increased workload for other staff, they’re describing a system where trained people can’t actually perform.[6] The problem isn’t training quality. The problem is that the system has no way to verify whether trained people possess the capability they’re supposed to have. Certifications and credentials are issued; capability remains unverified.

Second, assessment infrastructure doesn’t exist at regional scale. Enterprise hiring relies on either credentials (which don’t predict capability) or expensive, time-consuming assessments (which only large enterprises can afford). SMEs, which represent 98 per cent of Southeast Asian businesses, have no middle ground. They can’t afford individual assessments. They can’t trust credentials alone. Result: hiring becomes a guessing game.

Third, deployment pathways are unclear. Even when organisations identify capable talent, they lack systematic frameworks for rapid deployment. In Japan, 16 per cent of specialised professional, manager, executive, and technician (PMET) roles remain unfilled for six months or longer, not because capable people don’t exist, but because organisations can’t verify who is actually capable and move them into roles quickly. This deployment friction slows regional AI adoption.

These three gaps – signal integrity, regional assessment infrastructure, and deployment speed – are the hidden constraints on Southeast Asia’s AI leadership ambitions.

The framework: Capability-by-doing vs capability-by-credential

Building regional AI leadership requires distinguishing between two fundamentally different types of assessment.

Capability-by-credential is what currently exists. A person completes training, passes a test, receives a certificate. The credential signals that they completed the training program. It does not signal that they can actually perform the work under real conditions.

Capability-by-doing is what the region needs. This means assessing whether someone can actually do the work: solve novel problems, integrate with existing systems, perform under pressure, teach others. Capability-by-doing requires more than training completion; it requires evidence of actual performance ability.

The distinction transforms everything. Organisations moving from credential-based to capability-based assessment make fundamentally different hiring decisions. They identify hidden capability that credentials miss. They avoid hiring people whose credentials exceed their actual ability. They deploy talent faster because they know what people can actually do.

For Southeast Asia specifically, capability-by-doing assessment is the infrastructure gap that, once closed, unlocks regional leadership.

Also Read: You’re waiting for everyone to agree: The hourglass doesn’t care

How AI-powered capability assessment changes the game

Closing this gap requires infrastructure that didn’t previously exist. This infrastructure has three layers.

• Layer one: Deterministic signals. Traditional hiring captures keywords and semantic understanding, does someone know the vocabulary and concepts required? This is necessary but insufficient.
• Layer two: Demonstrated understanding. Can the person explain concepts in their own words and apply frameworks to new situations? This shows deeper mastery than credential completion.
• Layer three: Capability reasoning. This is where AI transforms the game. Using advanced language models specifically trained for capability assessment, organisations can now ask: Given this person’s demonstrated knowledge, work history, and reasoning, can they actually perform this role? Can they solve novel problems? Will they perform under pressure?

Layer three is what currently doesn’t exist at regional scale. It’s the infrastructure gap that prevents Southeast Asia from rapidly identifying and deploying genuine AI capability. It’s also the infrastructure that, once deployed, enables organisations to stop hiring based on credentials and start hiring based on demonstrated capability.

Building regional tech leadership: The path forward

Southeast Asia’s path to AI regional leadership requires three simultaneous moves.

First, governments must shift measurement metrics. Current training programmes measure completion rates and credential issuance. Governments should measure capability verification: Of people certified in AI skills, what percentage can actually perform AI work? This single metric shift transforms incentives across the entire training ecosystem.

Second, organisations must adopt capability-based hiring. Rather than filtering for credentials, organisations should assess demonstrated capability before hiring. This applies to SMEs as much as enterprises; capability assessment infrastructure must be affordable and accessible at regional scale.

Third, the region needs shared assessment infrastructure. Just as SkillsFuture created shared training infrastructure, Southeast Asia needs shared capability assessment infrastructure. This infrastructure should:

• Verify demonstrated AI capability (not just credential ownership)
• Be accessible to SMEs and enterprises alike
• Operate across national boundaries (Singapore, Japan, Korea, Taiwan, Vietnam, Indonesia)
• Enable rapid talent deployment across the region
• Create regional, verifiable signals about who can actually do AI work

This infrastructure is the missing piece. Without it, Southeast Asia remains talent-rich but capability-constrained. With it, the region becomes a global AI leadership centre.

Also Read: What AI safety researchers actually worry about

The competitive imperative

The timeline matters. China is rapidly building AI manufacturing and infrastructure capabilities. The US dominates AI research and development. India has established AI services and outsourcing leadership. Europe is building AI regulation and compliance infrastructure.

Southeast Asia’s opportunity is different: become the global leader in identifying, verifying, and rapidly deploying AI talent at scale. This isn’t about competing on research (the US leads) or manufacturing (China leads). It’s about building the human infrastructure that turns global AI innovation into global AI value creation.

But this opportunity has a window. Early movers in regional capability assessment infrastructure will define regional AI leadership for the next decade. Organisations that adopt capability-by-doing assessment now will have access to the best talent, fastest deployment, and highest competitive advantage as regional AI adoption accelerates.

Building the foundation

Southeast Asia has the talent pool. The region has the investment. The region has the government commitment and regulatory tailwinds. What the region lacks is the infrastructure to verify capability and deploy it effectively.

Building this infrastructure is the single highest-impact investment Southeast Asia can make in regional AI leadership. It’s not about training more people. It’s about finally being able to tell who is actually capable and moving them into roles where they can create value.

Regional AI leadership isn’t a function of how many people you train. It’s a function of how effectively you identify the truly capable and deploy them at scale. That’s the infrastructure gap Southeast Asia must close. That’s the foundation regional tech leadership requires.

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

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

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The post Southeast Asia’s AI talent and infrastructure: Building the foundation for regional tech leadership appeared first on e27.

For much of the last decade, “going global” was almost a rite of passage for startups. The formula seemed straightforward: build a product that solves a real problem, raise capital, expand into new markets, and let globalisation create the scale. Technology was increasingly borderless, capital flowed relatively freely, and businesses assumed that the world would become progressively more interconnected.

Over the past two years, however, that assumption has quietly changed. Trade tensions, semiconductor restrictions, sovereign AI strategies, and shifting investment priorities have begun reshaping the environment in which businesses operate. These developments often make headlines as geopolitical events, but for founders, they translate into very practical questions about where to build, who to partner with, and how to grow. I have not had to abandon international ambitions, but I have had to redefine what “going global” actually means.

The most significant business decision I made in the past year was not entering a new market. Instead, it was deciding to invest more deeply in building intelligence into my product before pursuing expansion. I founded a platform that tracks corporate events and strategic actions across listed companies, helping investors and business professionals understand developments that may influence investment decisions. Initially, the objective was relatively simple: make corporate information more searchable and easier to access. However, as I continued analysing thousands of announcements, I began noticing that many corporate decisions were no longer driven solely by commercial considerations.

Increasingly, companies were relocating manufacturing facilities because supply chains had become politically sensitive rather than simply cost inefficient. Strategic partnerships were being formed to satisfy local ownership requirements. Investments in semiconductor manufacturing, artificial intelligence infrastructure, renewable energy, and critical minerals were often influenced as much by national industrial policies as by market demand. Even financing decisions reflected changing global capital flows, with companies seeking investors whose strategic priorities aligned with shifting geopolitical realities.

This changed the direction of my own product. Rather than becoming another searchable database of announcements, it became increasingly important to explain why each corporate event mattered. Every announcement now carries additional context through impact assessments and strategic analysis, helping users understand not only what happened, but also what broader forces may have influenced the decision. As geopolitical fragmentation accelerates, I believe context has become significantly more valuable than information alone. Data is increasingly abundant; interpretation is becoming the real differentiator.

Also Read: The new map of growth: Where Southeast Asia fits in a fragmented world

These changes also made me rethink international expansion. In the past, founders often assumed that once a product worked well domestically, it could simply be replicated across multiple countries. Today, that assumption is becoming increasingly difficult to sustain. Different jurisdictions are introducing their own approaches to data residency, AI governance, cloud infrastructure, and digital sovereignty. Products that appear technically identical may require entirely different operating models depending on where they are deployed.

For businesses building AI-powered products, these considerations are no longer technical details left for engineers to solve after expansion. Decisions about where customer data is stored, which AI models can be deployed, which cloud providers are used, and how information is processed increasingly become strategic business decisions. Governments across Asia are investing heavily in sovereign AI capabilities and placing greater emphasis on domestic digital infrastructure. As founders, we therefore need to think about regulatory compatibility from the beginning rather than treating localisation as a problem to solve after entering a new market.

At the same time, I do not believe geopolitical fragmentation is purely a constraint. It also presents opportunities, particularly for Southeast Asia. For decades, the region has benefited from maintaining economic relationships with both East and West. While major economies are increasingly competing for technological leadership, Southeast Asia continues to occupy an important middle ground. Businesses here have developed experience operating across multiple legal systems, languages, cultures, and regulatory environments. That adaptability has become an advantage in itself.

Rather than attempting to replicate Silicon Valley or compete directly with larger technology ecosystems, Southeast Asian companies have an opportunity to specialise in navigating complexity. The region’s strength lies in its ability to connect different markets, understand diverse customer needs, and operate within varying regulatory frameworks. Those capabilities are becoming increasingly valuable as the global economy becomes more fragmented.

The shift is equally visible in capital markets. Investors today appear less focused on how quickly a company can expand geographically and more interested in how resilient the business will remain if external conditions change. Questions about supply chain dependence, regulatory exposure, customer concentration, and operational flexibility are becoming more prominent during funding discussions. Growth remains important, but sustainable growth supported by resilient business models is attracting greater attention than expansion at any cost.

Also Read: The alliance economy: How founders and investors should position in a fragmented world

This has influenced how I think about building my own business. Rather than optimising solely for rapid market expansion, I am more interested in building a platform that continues creating value regardless of changing political or economic conditions. If the rules of international business continue evolving, then products capable of helping businesses interpret those changes become even more relevant. In many ways, geopolitical fragmentation has strengthened the long-term rationale behind what I am building.

As a result, my definition of “going global” has changed. It no longer means trying to establish a presence in as many countries as possible. Instead, it means building something that remains valuable across different markets despite their differences. It means understanding local regulations before entering a market, designing products that accommodate varying policy environments, and recognising that expansion strategies will increasingly differ from one jurisdiction to another.

If I were advising founders beginning their journey today, I would encourage them to study policy, industrial strategy, and capital flows with the same attention they devote to competitors and customer acquisition. Markets are no longer shaped purely by consumer demand or technological innovation. Government priorities, geopolitical relationships, and regulatory developments are becoming equally influential in determining where opportunities emerge and where risks accumulate.

Globalisation has not disappeared, nor do I believe it will. What has changed is the assumption that one strategy fits every market. The future belongs to founders who understand that technology may be global, but regulation, capital, and strategic priorities are becoming increasingly local. Success will belong not only to those who build the best products, but also to those who best understand the environment in which those products operate.

In a fragmented world, information remains valuable, but context has become indispensable. That realisation has shaped the most important business decision I have made over the past year, and I believe it will continue shaping how many founders approach growth in the years ahead.

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

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The Generative AI revolution is here, but for many enterprises in Singapore and Southeast Asia, adoption has hit a hard wall. The barrier isn’t a lack of use cases; it is Data Security and Hallucination Control.

When dealing with highly sensitive domains (like Healthcare, Insurance, or Financial Data), passing raw payloads to external Large Language Models (LLMs) without strict guardrails is a compliance nightmare. Over the last few weeks, I set out to solve this exact problem by architecting a secure, multi-agent conversational platform on Google Cloud.

Today, I’m open-sourcing the reference architecture and the codebase.

The architecture: Defence-in-depth for LLMs

To build an enterprise-grade agent, you cannot simply connect a frontend directly to an LLM. You need a multi-layered security protocol. I leveraged GCP Sensitive Data Protection (SDP), Model Armour, and Vertex AI LLMOps Guardrails to create an impenetrable filtration layer before the data ever touches the Gemini 2.5 Flash agent.

• The PII redaction layer (SDP)

When a user inputs a prompt or uploads a medical document, the payload is intercepted by the GCP SDP engine. This engine uses custom inspection templates to hunt for specific Southeast Asian PII patterns (such as Singapore NRICs/FINs, local phone numbers, and names).

Before the LLM even sees the prompt, it is tokenised. For example, “Hi, my name is Syam, and my NRIC is S1234567A” becomes “Hi, my name is [PERSON_NAME], and my NRIC is [SINGAPORE_NRIC_FIN]”.

• The content filtration layer (Model Armour)

Even with PII redacted, the prompt must be scanned for malicious intent. I integrated GCP Model Armour as a firewall to detect prompt injections, jailbreaks, and toxicity. It scans both the inbound prompt and the outbound agent response to ensure the system cannot be manipulated into leaking internal system instructions.

• LLMOps guardrails: Fact-based constraints

In highly regulated sectors like insurance and healthcare, an AI system cannot give medical or financial advice. It is strictly restricted to providing fact-based details and retrieving policy information.

To enforce this, I implemented strict System Instructions and Guardrails managed through the Vertex AI LLMOps process. This acts as the final perimeter. If a user asks the agent for a medical diagnosis, the guardrails force the agent to politely decline and redirect the user to a human specialist. This virtually eliminates dangerous hallucinations.

• Agentic routing and Apigee integration

Once the sanitised prompt clears these three security layers, it hits the Agent Router. Using Google’s Agent SDK, the router dictates which specialised agent (e.g., Clinical Inquiry vs. Customer Support) should handle the request. These agents are wrapped in an Apigee API Gateway, allowing them to securely pull real-time enterprise data from internal databases.

Also Read: Singapore firms embrace agentic AI, but audit trails remain thin

Validating the architecture: GPT-as-a-judge

Building an agent is one thing; validating it at scale is another.

To prove this architecture works across the diverse linguistic landscape of Southeast Asia, I built a Batch Evaluation Platform. We ran simulated prompts through the pipeline in English, Cantonese, Malay, and Bahasa. Instead of manual review, I engineered an automated LLM-as-a-judge pipeline to score the responses based on relevance, harmfulness, and regional localisation accuracy.

Open source and next steps

As Singapore continues to push its Smart Nation agenda, securing AI workflows will be the defining challenge for our tech ecosystem. We cannot sacrifice privacy for innovation.

I have open-sourced the Terraform infrastructure and the backend codebase for this architecture on my GitHub. I encourage local developers and enterprise architects to fork it and adapt it for their own secure AI deployments.

• View the Infrastructure Repo here.
• View the Agent Security Framework here.

This article was originally published here. 

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

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The Philippines is embracing generative AI at remarkable speed. But unless we learn to question it, govern it, and retain ownership of our decisions, the technology meant to empower us may quietly weaken our ability to think and act for ourselves.

EVER asked ChatGPT to write an email, settle an argument, interpret a medical symptom, choose a career path, or tell you whether you were right?

For many Filipinos, artificial intelligence is no longer merely a search tool. It is becoming an adviser, tutor, editor, programmer, confidant, and sometimes even an authority figure. We turn to it because it is fast, articulate, endlessly patient, and available at any hour.

The benefits are real. Generative AI can help workers draft reports, analyse documents, generate code, summarise meetings, translate ideas, and overcome the anxiety of a blank page. It can widen access to information and improve productivity, especially for people who lack immediate access to specialists.

But there is a question we are not asking often enough: What happens when AI does not simply assist our thinking, but begins to replace it?

This is the emerging risk of situational disempowerment—the gradual erosion of a person’s ability to form accurate beliefs, make authentic value judgments, and act according to independently considered reasons.

The danger is not that AI suddenly takes control. It is subtler. It happens when a confident answer feels more credible than it deserves, when an agreeable chatbot validates a mistaken belief, or when a user follows a recommendation without understanding how it was reached.

Consider the workplace. A junior employee uses AI to finish a task in half the time. The output appears professional, and the deadline is met. Yet when a client raises a difficult question, the employee cannot explain, defend, or revise the work. Productivity improved on the surface, but capability weakened underneath.

This creates a dangerous productivity inversion: AI accelerates routine tasks while potentially making people less prepared for complex, ambiguous, and high-value work.

The same risk extends beyond the office.

A person may ask AI whether a partner is manipulative, whether a family member should be cut off, or whether a major financial decision is sensible. The model may provide a coherent answer based only on one side of the story. Because the response sounds calm and authoritative, the user may treat it as objective judgment rather than probabilistic text generated from incomplete information.

Also Read: The localisation gap: Why multilingual AI isn’t enough for APAC markets

There are three ways this can distort human autonomy.

• Reality distortion occurs when AI reinforces false or unsupported beliefs. Large language models can fabricate facts, express false confidence, or agree with a user simply because agreement produces a satisfying conversation.
• Value-judgment distortion happens when users allow AI-generated moral positions to override their own principles. AI can help clarify choices, but it should not silently become the source of our values.
• Action distortion occurs when people execute AI recommendations with little independent review—sending a message, filing a document, making an investment, ending a relationship, or taking some other consequential step they later regret.

At the centre of these risks is what may be called belief offloading: allowing AI not only to organise information, but to form and maintain beliefs on our behalf.

Humans have always delegated cognitive work. We use calculators, maps, search engines, advisers, and institutions. Delegation is not inherently harmful. The problem begins when we stop evaluating evidence because the system appears more informed, more articulate, or more certain than we feel.

This matters greatly in the Philippines.

Filipinos are among the world’s most enthusiastic users of generative AI. At the same time, the country continues to struggle with functional literacy, misinformation, digital scams, and unequal access to high-quality education. In such an environment, fluency of language can easily be mistaken for accuracy, while confidence can be mistaken for competence.

Cultural tendencies may also intensify the issue. Filipinos often place substantial weight on expertise, hierarchy, social harmony, and trusted relationships. When an AI system is framed as an expert, mentor, or companion, challenging its output may feel less natural—even when challenge is precisely what responsible use requires.

The answer is not to reject AI.

Also Read: What AI safety researchers actually worry about

Businesses, schools, and government agencies should instead develop a more disciplined model of human–AI collaboration.

Users must know what to delegate and what to retain under human judgment. They must learn to give clear instructions, examine sources, identify unsupported claims, disclose AI use when appropriate, and remain accountable for final decisions.

Organisations also need acceptable-use policies that identify approved tools, restricted information, required review procedures, and decisions that must never be fully delegated to an AI system. Without such rules, employees will continue using multiple platforms invisibly, creating not only cognitive risks but also privacy, security, and governance exposure.

Most importantly, AI fluency must not be reduced to prompt engineering. Knowing how to obtain a polished answer is not the same as knowing whether that answer is correct, ethical, appropriate, or aligned with one’s objectives.

The decisive skill of the AI era will not be generating more output. It will be preserving human discernment amid an abundance of plausible output.

AI should expand our capacity to think, not become an excuse to stop thinking. It should sharpen judgment, not substitute for it. And it should remain a tool whose recommendations we examine—not an authority whose conclusions we simply obey.

The future of AI in the Philippines will not be determined only by the sophistication of the models we use. It will depend on whether Filipinos remain active authors of their beliefs, values, and actions.

Let our AI assist us. But let us never surrender the responsibility to decide.

This editorial is adapted from the dissertation proposal “Situational Disempowerment in the Age of Generative AI: Examining Reality Distortion, Value Judgment Distortion, and Action Distortion Among Filipino Large Language Model Users.”

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For many Southeast Asian companies, selling across borders has become easier than getting paid across them.

A merchant in Singapore can source from Vietnam, sell to customers in Indonesia, pay a logistics partner in Thailand, and settle invoices with a platform in the US. The commercial opportunity is regional, even global. But the money still moves through a patchwork of banks, card networks, payment providers, foreign exchange desks, and local clearing systems that rarely speak to one another cleanly.

Also Read: Optimising cross-border payments for seamless APAC expansion

That gap has turned cross-border payments into one of the least glamorous but most consequential problems in the region’s digital economy. Fees can be hard to predict. Foreign exchange spreads vary. Settlement timelines differ by country and provider. Treasury teams often do not know exactly where cash sits at any given moment, or whether converting it today will be cheaper than waiting until tomorrow.

A new report by Sunrate and Mastercard, titled “Beyond Automation: Defining Agentic Global Payments”, argues that the next phase of payment technology will not simply automate existing workflows. Instead, it will use AI agents to make decisions across routing, liquidity, reconciliation, and foreign exchange in near real time.

The distinction matters. Traditional automation follows fixed rules: if an invoice is due, send a payment; if a transaction fails, try again through another channel. Agentic AI goes further. It refers to systems that can interpret changing conditions, weigh different options, and recommend or execute the best course of action within set guardrails.

In payments, that could mean choosing the lowest-cost route for a transaction, deciding when to convert currencies, identifying mismatches between invoices and receipts, or flagging only the exceptions that require human review.

The liquidity problem hiding in plain sight

The report identifies one pain point that will sound familiar to many finance teams: the “liquidity blind spot”. This refers to the lack of timely visibility into where cash is, what currency it is held in, and when it is needed.

For large corporations, this is a treasury headache. For startups and SMEs, it can be existential.

A regional e-commerce exporter may receive US dollars, pay suppliers in Chinese yuan, settle logistics bills in Thai baht, and cover payroll in Indonesian rupiah. If the company converts too early, it may lose out when rates move favourably. If it converts too late, it may face higher spreads or a cash crunch. If finance teams rely on end-of-day reports and manual spreadsheets, they are often reacting to yesterday’s position rather than managing today’s risk.

Agentic systems could reduce that lag. According to the report, AI agents can monitor FX trends and liquidity needs, then recommend or execute conversion timing based on real-time market data. In practice, this shifts treasury from periodic matching to continuous reconciliation. Instead of staff manually checking every payment and bank entry, the system reconciles routine flows and flags genuine discrepancies.

That may sound technical, but the business impact is straightforward: less trapped cash, fewer avoidable FX losses, and faster decisions about where to deploy working capital.

Why APAC is fertile ground

The Asia Pacific region is a particularly relevant test bed for this model because its trade and payment flows are both fast-growing and fragmented.

The report notes that commercial card transaction volume in the travel segment alone is growing at a 28 per cent compound annual growth rate from 2023 to 2025. Travel is one of the clearest examples of why cross-border payments are difficult in this region. Online travel agencies, hotel operators, airlines, destination management companies, and corporate travel platforms may operate across dozens of markets, currencies, and settlement arrangements.

Also Read: How fiat and crypto are redefining cross-border payments

A booking made in Malaysia for a hotel in Japan through a Singapore-based platform may involve several parties before the final merchant receives funds. Each leg can add cost, delay, or data loss.

The same pattern appears in other sectors central to Southeast Asia’s startup economy: B2B marketplaces, logistics, software-as-a-service, gaming, creator platforms, and cross-border e-commerce. These businesses scale by connecting demand and supply across markets. Their finance operations, however, often become more complex with every new country added.

Emerging markets across APAC, Latin America, and the Middle East and Africa are seeing commercial card transaction growth of more than 20 per cent, according to the report. That growth creates a larger data trail, but also more routing choices and more operational risk. Static payment setups are less suited to this environment because fees, failure rates, FX conditions, and local payment rails can change quickly.

From fixed rails to intelligent routing

One of the report’s central ideas is “Intelligent Payment Routing”. In simple terms, it means allowing AI agents to select the most efficient provider, payment rail, or route for a transaction based on the transaction type, market conditions, and historical performance.

Today, many companies still use static routing. A payment to one country goes through a preferred provider, while a payment in another currency follows a pre-set bank channel. That may be manageable at low volumes, but it becomes inefficient as a business expands across markets.

Intelligent routing could compare options dynamically. For example, it may decide that one provider is cheaper for a low-value supplier payout, while another is more reliable for high-value settlement. It may avoid a route with historically high failure rates during local banking cut-off times. It may select a card rail for speed in one case and a bank transfer for cost in another.

For Southeast Asian companies, this flexibility is especially useful because regional expansion rarely follows a neat path. A startup may begin in Singapore, add Indonesia, then serve the Philippines, Thailand, and Vietnam within a short period. Each market brings its own banking infrastructure, regulatory expectations, payment behaviours, and currency considerations.

Agentic payment systems will not remove that complexity entirely. But they can help businesses manage it without building large treasury teams before they have the scale to justify them.

The working capital argument

The strongest case for agentic payments may not be lower fees alone. It is working capital.

The report states that finance teams using intelligent automation in accounts receivable can reduce manual effort by up to 40 per cent. It also notes that leading platforms integrating AI agents can help companies reduce Days Sales Outstanding, or DSO, by up to 12 days and cut manual follow-ups by 50 per cent.

DSO measures how long it takes a company to collect payment after a sale. A reduction of 12 days can be meaningful for a growing business. It means cash arrives earlier, reducing the need for short-term borrowing or delaying supplier payments. In a funding environment where venture capital is more selective than it was during the 2021 boom, operational cash efficiency has become a competitive advantage.

This is particularly relevant in Southeast Asia, where many startups serve SMEs or operate in sectors with thin margins and uneven payment cycles. Faster collections and better reconciliation can give founders more room to invest in inventory, marketing, hiring, or market expansion.

Automation with guardrails

The promise of agentic AI in payments is significant, but it also raises practical questions. Finance teams will need clear controls over what AI agents can execute autonomously and what requires approval. Regulators will expect audit trails. Businesses will need explainability, especially when a system chooses one route, provider, or FX timing over another.

There is also the matter of trust. Companies may be willing to let AI recommend a conversion window or flag suspicious reconciliation items. They may be slower to allow autonomous execution of large transactions without human oversight.

Also Read: Singapore’s regulatory vision is shaping cross-border payments in Asia: Report

That suggests adoption will likely be gradual. AI agents may first handle low-risk tasks such as matching invoices, suggesting routes, detecting anomalies, and preparing payment recommendations. Over time, as accuracy improves and controls mature, they could take on more direct execution.

The direction, however, is becoming clearer. Cross-border payments are no longer just a back-office function. For companies expanding across Asia Pacific, they shape margins, customer experience, supplier relationships, and cash flow.

If agentic AI can make global payments less opaque and more responsive, it could help Southeast Asian businesses compete beyond their home markets without being slowed by the plumbing underneath.

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Every time someone hops out of a Grab without touching their wallet, or taps “pay later” on a Shopee checkout, they are using a piece of financial infrastructure they never consciously chose. There is no app to download, no form to fill, no trip to a bank branch. The transaction simply happens, folded invisibly into an experience that was never meant to be about money in the first place.

This is embedded finance, and according to Abdul Mikael, it has become the defining undercurrent of Southeast Asia’s digital economy.

Banking you don’t notice

“Embedded finance is essentially ‘invisible banking’,” Mikael explains. “It integrates financial tools like payments, credit, and insurance directly into everyday, non-financial platforms so transactions happen effortlessly in the background.”

Also Read: Security implications of embedded finance in non-financial platforms

That description captures why the region’s super-apps have become such fertile ground for the model. A Grab ride ends without a wallet in sight. A Shopee order gets split into instalments through ShopeePayLater at the point of checkout. In each case, the financial service arrives precisely when it is needed, with no detour through a traditional banking app.

When every company becomes a bank

The consequence of this shift is that companies with no history in finance are increasingly behaving like financial institutions, whether they intend to or not. Mikael points to Starbucks as the clearest illustration outside the region. Through its app’s preload feature, the coffee chain holds roughly US$2 billion in customer balances, a sum larger than the total deposits of many small-to-midsize traditional banks.

“Customers are essentially giving Starbucks an interest-free loan to fund its working capital,” he says, “while Starbucks generates hundreds of millions in high-margin revenue from interest on that float and unspent ‘breakage’ balances.”

E-commerce platforms and gaming apps across Southeast Asia are running the same playbook when they introduce stored-value wallets, buy-now-pay-later options, or reward systems: eliminating third-party processing fees, deepening customer stickiness, and quietly converting everyday user activity into a self-funding financial engine.

The trap of bolting finance on too soon

For founders eager to follow suit, Mikael’s advice is blunt: resist the urge to rush. The first real step, he says, is to “meticulously map the user journey and target a specific friction point, like checkout drop-offs or delayed payouts, where embedded finance provides immediate, seamless utility.”

Also Read: Why embedded finance is critical to Southeast Asia’s digital future

The mistake he sees most often is what he calls premature financialisation — treating credit, BNPL, or wallet features as a quick monetisation trick before the underlying product has found genuine traction. “Embedded finance is an accelerant for user experience, not a band-aid for poor product design,” he says. “If your core non-financial offering doesn’t already resonate with customers, introducing a financial tool won’t fix it.”

Security, he adds, cannot be an afterthought either. The smarter route is partnering with providers that already hold the necessary regulatory licences and maintain standards such as PCI-DSS and automated KYC, rather than attempting to build bank-grade compliance from scratch.

One region, many speeds

Southeast Asia’s diversity complicates any attempt at a single regional strategy. AND Solutions operates across 11 countries, with a strategic focus on the Philippines, Thailand, Indonesia, and Vietnam, and Mikael is candid about what that has taught him: “A copy-and-paste playbook will fail.”

In mature markets like Singapore, existing banking infrastructure means new technology mostly adds convenience. In emerging markets, that infrastructure barely existed for large parts of the population. “Instead of building physical branches or issuing credit cards to millions of unbanked citizens, these regions leapfrogged the card phase entirely,” Mikael notes, moving straight to mobile-first rails built on e-wallets, telecom networks, and national QR systems.

Post-pandemic, consumer priorities have shifted too. Ease of use and constant access, once selling points, are now simply expected. “Security has emerged as the primary focus for consumers today,” he says, a direct response to the wave of fraud and phishing that accompanied the pandemic-era surge in digital payments.

Where the real money is

With embedded finance revenues in Singapore alone projected to reach US$7.85 billion by 2029, Mikael sees the sharpest opportunities not in flashy consumer verticals but at the intersection of B2B software and AI-driven infrastructure, using real-time operational data to offer instant trade credit, automated cash-flow tools, and predictive underwriting.

He is equally clear about what will separate winners from also-rans. “The winners in this space won’t be platforms with the lowest processing rates,” he says, but those using proprietary data to deliver financing at precisely the right moment, transforming embedded finance into “an invisible ecosystem moat” rather than a mere transaction fee.

Inclusion, done responsibly

Perhaps the most consequential frontier, though, is financial inclusion. Traditional lending’s reliance on formal credit history has long excluded large numbers of Southeast Asia’s individuals and SMEs. AND Solutions’s recent collaboration with B-Quik, Thailand’s leading automotive service provider, aims to embed financing directly into that network, using AI and alternative data to assess risk beyond a single credit score.

Also Read: Embedded finance will drive financial growth and sustainability in India

“Bringing technology closer to everyone means making financial services available where people already live, work, and do business,” Mikael says — but he is quick to add a caveat: “AI should be transparent, explainable, and continuously monitored to ensure fair and consistent decisions.”

As embedded finance matures beyond payments into AI-powered lending, Mikael’s closing thought feels like the clearest summary of where the industry is heading: “The future of embedded finance won’t be defined by how many financial products a platform offers. It will be defined by how intelligently those products are delivered at the right moment.” In a region racing to digitise, that distinction may prove to be the only one that matters.

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Acrab, a Singapore-headquartered technology company building agentic AI compute infrastructure, has raised US$130 million in a Series B round, as investor interest continues to shift from AI applications to the hardware and systems needed to run them.

The round was led by existing backers Vertex Ventures SEA & India and Vertex Growth, with participation from institutional investors across Europe and Southeast Asia. It follows Acrab’s recent emergence from stealth and the launch of its Agent Box platform, powered by the company’s first-generation GΞLIX 1 chip.

The company said the fresh capital will go towards scaling its products, expanding its ecosystem, and developing its next-generation computing platform. Acrab added that it sees “visible paths” to industrial deployments across multiple domains and expects to start generating revenue in 2026.

Also Read: Exploring the ‘Phygital’ world where digital and physical realms converge

The fundraise comes after Acrab’s previous US$350 million fundraising in June, an unusually large amount for a company that has only recently stepped into public view. The new Series B suggests that its investors are backing a long-cycle infrastructure play rather than a conventional software startup looking for rapid commercial rollout.

At the centre of Acrab’s pitch is a simple but ambitious claim: as AI agents become more capable and more personal, they will need to run closer to users, machines, factories, vehicles and devices — not only inside remote cloud data centres.

Moving AI from the cloud to the edge

Acrab is building what it describes as a full-stack AI computing platform, combining purpose-built silicon, edge AI systems, and software orchestration. In practical terms, that means the company is not merely designing chips or building an AI device. It is trying to control the full computing layer needed for AI agents to operate locally.

Its first-generation system-on-chip, GΞLIX 1, is designed to run large language models at the 100 billion parameter scale on local hardware. Parameters are the internal values that help an AI model process and generate outputs; as a rough rule, larger models tend to be more capable but also require more computing power and memory to run.

Today, much of that work happens in the cloud. A user types a prompt into an AI application, and the actual computation takes place in a data centre owned by a hyperscaler or AI infrastructure provider. That model has powered the first wave of generative AI adoption, but it comes with trade-offs: latency, connectivity dependence, energy costs, data privacy concerns, and rising cloud bills.

Edge AI attempts to solve some of those problems by moving computation closer to where data is created. For Southeast Asia, this is not a small distinction. The region has thousands of factories, ports, hospitals, logistics networks, plantations and city systems where connectivity can be uneven, data may be sensitive, and real-time response matters.

A locally running AI agent in a manufacturing plant, for example, could monitor equipment, interpret images or sensor data, and trigger actions without sending every piece of information to a cloud server. In healthcare, on-device AI could support analysis while keeping patient information within a hospital’s own systems. In logistics, AI models running at the edge could help route vehicles, inspect goods, or manage warehouse operations even when networks are congested.

Acrab’s Agent Box is its first visible product in this direction. The company describes it as a personal edge AI system that supports local large-model inference, persistent memory, multimodal interactions, and agent orchestration on-device. Inference refers to the process of running a trained AI model to produce an answer or action. Multimodal interaction means the system can process more than one type of input, such as text, image, voice or video.

Also Read: AI infrastructure: The unsung hero of technological innovation

The “agent orchestration” element is important. The next phase of AI is not just about chatbots responding to questions. It is about software agents that can plan tasks, use tools, remember context, and act across workflows. That creates a heavier infrastructure burden, especially if users expect these agents to be always available, private, and responsive.

Why this matters in Southeast Asia

Southeast Asia has been a fast adopter of AI software, but the region remains heavily dependent on global computing infrastructure. Most startups building AI products still rely on cloud providers and overseas chip supply chains. As demand for AI workloads grows, access to compute has become a strategic constraint, particularly for smaller companies that cannot compete with global technology giants for the latest graphics processing units.

Singapore has positioned itself as a regional hub for AI, semiconductors, data centres, and deeptech financing. That makes it a natural base for companies such as Acrab, even if the market for its products will likely be global from the start. The city-state has the capital networks, research talent, corporate customers and policy support needed for infrastructure-heavy ventures. At the same time, the broader region offers industrial use cases where edge AI could prove useful beyond consumer gadgets.

The challenge is that AI hardware is expensive, slow to commercialise, and difficult to scale. Designing silicon is only one part of the problem. Companies must also secure manufacturing capacity, build developer tools, support software frameworks, manage thermals and power consumption, and convince customers to trust a new computing architecture.

This is where Acrab’s full-stack approach could either become an advantage or a burden. Owning more of the system may allow tighter optimisation between chip, device and software. But it also means the company is taking on several hard problems at once.

A crowded global race

Acrab is entering a field dominated by some of the world’s best-capitalised technology companies. Nvidia remains the clear leader in AI accelerators, with its GPUs powering much of the cloud AI boom. AMD and Intel are trying to capture more of the AI infrastructure market, while Qualcomm, Apple and MediaTek are pushing more AI processing into phones and personal devices.

There is also a growing group of AI chip specialists and infrastructure startups, including Cerebras, Groq, SambaNova, Tenstorrent and Etched, each attacking different parts of the performance, cost and efficiency equation. Some focus on data centres, some on inference, and others on specialised architectures for transformer models, the foundation behind many modern large language models.

Acrab’s distinction, at least from what it has disclosed, lies in its focus on agentic edge infrastructure: running large AI models locally while supporting persistent, on-device agents. That puts it at the intersection of several markets — chips, personal AI devices, enterprise edge systems and AI operating layers. It is a promising but unforgiving position.

From capital to commercial proof

The next test for Acrab will be less about fundraising and more about execution. Deeptech companies often raise large sums before revenue because the upfront cost of research, engineering and supply chain development is high. But investors will eventually expect proof that customers are willing to deploy the technology outside pilots and controlled demonstrations.

Acrab says it expects revenue within 2026 and sees industrial deployment opportunities across multiple domains. That timeline gives the company room to refine its platform, but it also places it in a fast-moving race. AI model sizes, inference techniques and chip architectures are evolving quickly. What looks cutting-edge today can become outdated within a product cycle.

Still, the direction of travel is clear. As AI agents move from novelty to everyday infrastructure, the question of where they run will become more important. Cloud data centres will remain central to training and heavy workloads, but not every AI task can or should travel back to the cloud.

Also Read: Securing Agentic AI for Singapore enterprises: A reference architecture

For Southeast Asia, where digital adoption is high but infrastructure conditions vary sharply across markets, edge AI could become more than a technical preference. It could be the difference between AI that works only in ideal environments and AI that can operate in factories, clinics, farms, ports and homes across the region.

Acrab’s US$130 million Series B is therefore not just another AI funding announcement. It is a bet that the next computing platform will not be defined solely by bigger data centres, but by intelligent systems that sit closer to the real world.

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