The conversation around AI agents has moved quickly from demos to organisational design. Microsoft’s 2025 Work Trend Index for Singapore reported that 56 per cent of Singapore leaders were already using agents to fully automate workstreams or business processes, while 46 per cent expected their teams to build multi-agent systems.

I understand the appeal. I built an internal AI office with 21 defined roles across strategy, finance, marketing, sales, customer success, delivery, engineering, design, quality assurance, security, research and data.

I expected the hard part to be technical. It was not. The harder questions were managerial: Who owns the work? Who reviews it? What can an agent decide on its own? When does a human step in?

Most of my 21 roles are real and used in live work today, but most orchestration is still interactive. That distinction matters because the biggest lesson was not how to remove humans. It was how to design responsibility around machines.

The first mistake: Capability is not ownership

My earliest agents were too broad. Help with marketing or help with development sounded reasonable because modern models can do many things.

The problem was not output quality. It was ownership.

If one agent can research, write, publish and evaluate the result, who is actually responsible for the job? If something is wrong, where does the failure sit?

I eventually stopped defining roles by what the model could do and started defining them by what the role should own.

A writing role writes. A publishing role prepares distribution. A research role scouts for information but does not install what it discovers. A builder builds, but does not declare its own work production-ready.

That sounds obvious in a human company. With AI, it is easy to forget because the same underlying model may be capable of doing all of those jobs.

Also Read: When a slot opens, let the AI agent act – within limits

The second mistake: Letting one agent close the loop

I also learned that an agent should not be allowed to create, review and approve the same important output.

When a builder tests its own work and then declares it ready, the process can look efficient while removing the independent challenge that catches weak assumptions.

So I separated three responsibilities for higher-impact work: builder, reviewer and acceptor.

The builder produces the work. A separate role tests or reviews it. Final acceptance of important client-facing, production or irreversible actions still comes back to a human.

Not every task needs three layers. Drafting an internal note does not need the same controls as deploying software or sending something to a client.

The point is proportionality. The higher the impact of being wrong, the more important independent review becomes.

The third mistake: Treating autonomy as a maturity score

At first, I saw autonomy as progress. If an agent could do more without asking me, the system felt more advanced.

I no longer think that way.

The better question is: what happens if this action is wrong?

Singapore’s updated Model AI Governance Framework for Agentic AI uses a similar risk-based approach. One case study in the framework tiers actions by severity, reversibility and the feasibility of human oversight. Low-risk and reversible actions can be automated. Moderate-risk actions require human approval. High-risk actions with limited reversibility may be blocked entirely.

That logic changed how I design workflows.

An agent drafting an internal summary can have wide freedom. An agent changing permissions, deploying code, committing money or sending an external communication should face a much higher bar.

More autonomy is not always better. Appropriate autonomy is better.

The fourth mistake: Managing the agent but not its access

An agent is not just a prompt.

It may have access to tools, databases, files, connectors, scheduled jobs and external services. As my system grew, I realised the real management problem was not simply how many agents do I have. It was what can each of them touch.

That pushed me to maintain an inventory of the agents, tools and capabilities around them.

This aligns with Singapore’s agentic AI framework, which recommends bounding agents’ powers upfront, including their autonomy and access to tools and data.

For founders, this is easy to overlook because experimentation moves quickly. A useful prototype can become part of an operating process before anyone has paused to document what permissions accumulated around it.

I now treat that inventory as basic operating hygiene, not administrative paperwork.

Also Read: From KYC to KYA: how AI agents are reshaping payment risk

The most important lesson: Humans did not disappear

The fully unattended version of my AI office is still a work in progress.

Some workflows run automatically. Many still begin with a human instruction. Important outward actions still need approval.

I once saw that as a sign the system was unfinished. Now I think the better goal is not zero humans. It is humans only where judgement, accountability or relationships genuinely matter.

The AI roles can absorb repeatable work, prepare options, check outputs, monitor systems and move tasks forward. The human role becomes narrower but more important: direction, trade-offs, relationships and final accountability.

That is also why I am cautious with the term AI employee. An employee is not valuable because they can perform many tasks. They are valuable because there is clarity around what they own, what authority they have and how their work fits with everyone else.

What I would tell another founder

Before adding another agent, I would ask four questions:

• What job does this agent actually own?
• What systems and data can it access?
• Who independently checks important work?
• Which actions can it take without a human, and why?

If those answers are unclear, adding more agents does not create an AI workforce.

It creates more capability without more management.

That was the biggest change in my thinking. I started by trying to build better agents. I ended up redesigning the organisation around them.

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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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Quantinuum, one of the world’s leading quantum computing companies, has already put a number on it: a trillion-dollar market waiting to be unlocked once fault-tolerant quantum computing arrives. That’s not a typo, and it’s not a crypto-style promise built on speculation — it’s an estimate built on real, quantifiable industries: drug discovery, materials science, chemicals, energy and finance, where a single better answer can be worth billions on its own. The uncomfortable question for founders, investors and policymakers in Asia is the same one many asked too late about AI and too late (or too early, and badly) about crypto: will you be building the picks and shovels for this boom, or reading about it after the fact?

Unlike the crypto cycle, this isn’t hype chasing a use case. And unlike the early days of AI, the economic bar for quantum computing is brutally explicit: DARPA and industry alike are converging on one test — a quantum calculation only counts commercially once it demonstrably saves more money than it costs to run. A pharmaceutical company that spends billions developing a single drug, for instance, could justify an extraordinarily expensive quantum calculation if it meaningfully cuts the odds of a failed candidate. That is the bar quantum computing has to clear before it can have its own “ChatGPT moment” — and it’s a far harsher bar than the one artificial intelligence had to clear.

Quantum computing is no longer purely theoretical. The harder question is whether it can become economically useful. For Asia, the opportunity may be less about owning quantum computers and more about building the industries, infrastructure and applications around them.

Why quantum computing’s commercial breakthrough won’t look like AI’s

Artificial intelligence had been around for decades before ChatGPT made it commercially legible. Neural networks existed. GPUs existed. Large language models existed. What changed was that several prerequisites converged at roughly the same time: sufficient computing power, enormous datasets, scalable cloud infrastructure, better algorithms, usable interfaces, and a business model that let millions of people access the technology without understanding the underlying mathematics.

Quantum computing may be approaching its own equivalent transition. But there’s an important distinction. AI became useful while remaining imperfect. Quantum computing must overcome a much harsher economic constraint: its output must be valuable enough to justify an extraordinarily expensive physical machine.

DARPA has put the question unusually clearly. Its Quantum Benchmarking Initiative is trying to determine whether a quantum computer can achieve “utility-scale” operation by 2033 — meaning its computational value exceeds its cost. That may ultimately be the only quantum benchmark that matters.

Quantum today looks a little like AI before the commercial explosion

There’s a useful, if imperfect, analogy. AI progressed roughly like this:

Academic research → specialist systems → cloud APIs → foundation models → consumer applications → enterprise infrastructure

Quantum computing could follow a parallel but distinct path:

Physics experiments → error-corrected logical qubits → specialist scientific applications → cloud-accessible accelerators → industry workflows → national computational infrastructure

The difference is that AI could run on increasingly commoditised silicon. Quantum computing requires extremely specialised infrastructure: superconducting systems may need temperatures close to absolute zero; trapped-ion machines need lasers and ultra-high vacuum systems; neutral-atom machines require sophisticated optical control. The result is that the quantum revolution is unlikely to put a quantum computer on everyone’s desk.

Also Read: AI, quantum computing and the future of cyber threats

Instead, picture a future cloud architecture where a business application sits above an AI orchestration layer, which in turn routes work across CPUs, GPUs and QPUs — CPUs for business logic, GPUs for AI models, and QPUs for quantum chemistry or optimisation — before producing a single business output. Most users may eventually consume quantum computing without ever knowing a quantum processor was involved. That’s probably the real commercial end state.

The quantum roadmap: From laboratory to invisible infrastructure

Any timeline remains speculative, but current company roadmaps provide useful boundaries. IBM’s current hardware roadmap targets its Starling fault-tolerant system for 2029, with 200 logical qubits and 100 million quantum gates. Quantinuum’s published roadmap targets universal, fully fault-tolerant quantum computing by the end of the decade. These are company targets, not guarantees.

DARPA provides a useful external counterweight to vendor roadmaps: its 2033 utility-scale test is explicitly designed to separate industrial usefulness from hype. On the hardware side, Google’s Willow chip demonstrated “below-threshold” error correction in late 2024 — a long-sought milestone where adding more physical qubits makes a logical qubit more reliable rather than less, which is itself a prerequisite for any of these roadmaps to hold.

Five prerequisites have to converge before quantum computing pays off

• Fault tolerance must become economically practical. Physical qubits are noisy. Useful machines need logical qubits created using error correction. The commercial question isn’t whether error correction works in principle, but how much physical infrastructure, energy, control hardware and runtime are required to produce one reliable logical computation.
• Useful algorithms need to appear. Having a quantum processor without useful algorithms is analogous to having GPUs without deep learning. The most commercially important breakthrough may come not from hardware but from discovering algorithms that transform high-value industries.
• Classical computing has to lose somewhere. Quantum doesn’t compete against computers from 2020. It competes against whatever GPUs, supercomputers, AI models and optimisation software exist when fault-tolerant quantum systems arrive. The baseline is a moving target.
• Developers need abstraction. Most software engineers will never design quantum circuits. Commercial adoption requires APIs, orchestration layers and hybrid workflows that route only the structurally suitable part of a problem to the QPU.
• The economics must close. The final equation is simple: the economic value of the quantum result must exceed the cost of the QPU, HPC, energy, talent, integration, sampling and error correction. Quantum can be scientifically revolutionary while still being commercially irrational.

In practice, a hybrid workflow is emerging: AI decomposes a problem, classical HPC narrows the candidates, a QPU tackles the hard quantum subproblem, AI interprets the output, and a human validates the result before it’s acted on.

Also Read: The AI-quantum collision: Navigating the 2026 infrastructure inflection point

Where the money may actually be in quantum computing

The strongest early applications for quantum computing use cases share one characteristic: a single computational answer can be worth enormous amounts of money.

For the Philippines example, see DOST-PCIEERD’s ongoing STArQE quantum algorithms project for EV and renewable-energy planning. DOST-PCIEERD’s 2026 programme update also frames quantum technology as a national capability-building priority for future applications in energy, health, defence and information technology.

Who is leading the global quantum computing race?

There’s no universally accepted national ranking, because countries lead in different layers of the stack. The broad competitive map looks like this:

Singapore’s commitment is unusually concrete for Southeast Asia. The Singapore National Quantum Strategy has close to SG$300 million (US$233 million) set aside under RIE2025 for quantum research, talent and coordinated capability building.

What does a quantum-enabled Singapore actually look like?

Singapore’s geography makes it an unusually good candidate for early application. It’s small, urbanised, capital-rich, highly networked and concentrated around sophisticated sectors.

A bank doesn’t need to own a quantum computer. Its risk platform could send an unusually hard computational subproblem to a regional quantum service, sitting on top of a national HPC and quantum cloud. A pharmaceutical laboratory could use AI to generate molecules, conventional computing to eliminate obvious failures, and quantum processing for the subset where classical modelling becomes prohibitive. A port system could combine AI forecasting, classical optimisation and quantum algorithms only for hard search spaces where there’s a demonstrated advantage.

Also Read: Quantum computing’s double-edged sword could threaten cybersecurity: Report

Singapore’s advantage would be density: finance, biotech, government, universities, data centres and advanced infrastructure exist within one compact geography. Its national play may therefore be to become the application, finance, governance and integration layer for quantum computing in Southeast Asia, rather than to manufacture every layer of the stack itself.

The Philippines needs a very different quantum computing strategy

The Philippines should probably not copy Singapore. Its competitive advantage — and problem set — is almost the opposite: a geographically fragmented archipelago of more than 7,600 islands, uneven infrastructure, and large national-scale coordination problems across energy, logistics, and climate and disaster response.

Consider energy. The Philippines must coordinate island grids, renewable generation, storage, transmission limits, typhoon exposure, fuel imports, electricity demand and future EV infrastructure. AI can forecast demand, weather and failures. Classical optimisation can solve most operational decisions. A quantum service should be invoked only when a specific combinatorial or physical problem demonstrably exceeds the economic performance of classical computing.

The country is already building capability rather than waiting for mature hardware. DOST-PCIEERD has identified quantum technology as a national R&D priority and is currently funding projects including quantum-based forecasting and optimisation for the electric power grid.

The AI lesson quantum computing cannot afford to ignore

AI created enormous value. It also gave us benchmark inflation, hallucination, opaque models, exaggerated capabilities, concentration of compute and deployment ahead of governance. Quantum has an opportunity to avoid some of this by being more precise about what “advantage” actually means.

Only the fourth consistently creates a sustainable commercial market. A machine that performs a specialised calculation one million times faster but costs one billion times more has produced an impressive experiment, not an economic revolution.

Also Read: Quantum’s inflection point: Why the smart money is watching now

What to actually watch over the next decade

The most important quantum headline will probably not be “Company X reaches one million qubits.” A more consequential headline would be: “A pharmaceutical company identifies a commercially successful molecule using a quantum calculation that could not economically have been performed classically.”

At that point, investment follows. Software ecosystems emerge. Cloud providers commoditise access. Consultancies build implementation practices. Universities produce specialists. Startups stop selling “quantum” and start selling better batteries, new medicines, cheaper chemicals and lower financial risk.

The commercial endgame for quantum computing

The likely end state is a division of labour between human, AI, CPU, GPU and QPU — each solving the class of problem for which it has a structural advantage. AI determines what to investigate. Classical computing handles the overwhelming majority of computation. Quantum processors tackle narrow problems where quantum mechanics provides an economically meaningful advantage. Humans determine whether the answer should be acted upon.

For Singapore, that could create a new regional infrastructure and services industry. For the Philippines, it could offer access to computational capabilities that would be prohibitively expensive to build domestically.

The commercial quantum era begins not when the machines become powerful. It begins when someone can prove that not using one costs more.

Author’s note: Company roadmaps are forward-looking statements and are presented as targets, not independently guaranteed outcomes. The commercial timeline in this article is an analytical scenario based on current roadmaps and public evidence, not a prediction of certainty.

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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 Quantum’s ChatGPT moment is coming — and it’s worth trillions appeared first on e27.

Singapore has placed 25 companies on the Best Workplaces in Asia 2026 list, the highest count in Southeast Asia, at a time when employers across the region are fighting harder to hold on to skilled workers.

The list, published by Great Place To Work, ranks 200 companies across Asia based on employee survey responses. Of the Singapore-linked companies recognised this year, 20 are in the large-employer category, covering firms with at least 500 staff in Asia, while five sit in the small and medium category, for companies with 50 to 499 employees.

Also Read: Why building a people-first work culture in HR tech matters more than ever in Southeast Asia

That makes Singapore the fifth-largest contributor to the Asia list by company count, behind the UAE with 42, India (40), Greater China (39), and Japan (34). Great Place To Work’s Asia region includes the Gulf, which explains the UAE’s position at the top.

Among Southeast Asian markets, Singapore narrowly led Vietnam, which had 24 companies on the list, followed by the Philippines with 20, Indonesia with 19, Thailand with 18, and Malaysia with 15. Together, the six Southeast Asian markets accounted for 121 of the 200 recognised workplaces.

The ranking arrives in a year when workplace culture is no longer a soft metric for companies in the region. Aon’s 2025 Salary Increase and Turnover Study, which surveyed more than 700 businesses across six Southeast Asian markets between July and September last year, projects that 19.3 per cent of skilled Singapore workers will change jobs during 2026. That is the second-highest expected skilled-worker turnover rate in the region, behind only the Philippines at 20 per cent.

The pressure is made sharper by salary expectations. Singapore employers are budgeting pay rises of 4.3 per cent, the lowest among the six Southeast Asian markets covered by Aon and below the regional average of 5.3 per cent. For founders, operators, and HR leaders, that raises a familiar problem: when companies cannot compete on pay alone, they have to compete on trust, fairness, flexibility, and the quality of day-to-day management.

Real estate makes a rare appearance

One of the more striking features of Singapore’s showing is the presence of two real estate companies, a sector that barely appears in Southeast Asia’s workplace rankings.

Co-working operator JustCo ranked fifth among small and medium workplaces in Asia, while Pontiac Land Group placed 97th in the same category. They were the only real estate entrants from Southeast Asia. Indonesia, Malaysia, the Philippines, Thailand, and Vietnam had none.

Across the whole Asia list, real estate accounted for just 11 places, six of them held by UAE companies. Singapore was the only market outside the Gulf and Japan to have more than one real estate company recognised.

That matters because property has not typically been viewed as a talent magnet in the same way as technology, financial services, or consumer brands. In Singapore, however, the sector sits close to several major shifts: hybrid work, premium office demand, hospitality-linked real estate, and the changing use of commercial space after the pandemic. JustCo’s appearance also points to how flexible workspace operators are trying to position themselves not simply as landlords, but as workplace experience companies.

Biotechnology and pharmaceuticals formed another visible Singapore cluster. AbbVie ranked fourth in Asia, Merz Aesthetics ranked ninth among small and medium workplaces, and Amgen came in 50th. Thailand matched Singapore’s count in this sector, with the same three companies appearing on its national list.

Also Read: Growth meets purpose: Rethinking impact in startup culture

IT remained the largest single group in Singapore’s tally, with six companies. These included Cisco, which ranked third in Asia, Visa at 39th, and Mastercard at 65th. Manufacturing and production followed with four Singapore entrants, while hospitality and biotechnology had three each.

Overall, Singapore’s 25 companies covered 10 industries, slightly less broad than Vietnam’s 14 industries from a similar total.

The companies that climbed

Several Singapore entrants improved their regional positions from last year.

Visa posted the largest gain among Singapore companies, rising 19 places from 58th in 2025 to 39th in 2026. Micron Technology climbed 12 places, from 19th to seventh, entering Asia’s top 10. Amgen also gained 12 spots, moving from 62nd to 50th.

Other climbers included AbbVie, which rose from seventh to fourth; Cisco, which moved from fifth to third; Capella Hotels and Resorts, which went from 12th to 10th; and Marriott International, which edged up from third to second.

Hilton held first place in Asia for the second consecutive year and also appears on Singapore’s list. It was followed regionally by Marriott International, Cisco, AbbVie, and DHL. All five have Singapore operations recognised in the 2026 ranking, reflecting the Republic’s role as a regional base for many multinational groups.

At the same time, Singapore’s list was not only a story of established global names. Ten of its 25 companies were new to the Asia ranking: Allianz, Experian, Zim Integrated Shipping Services, Mastercard, Jebsen & Jessen, Heineken, JustCo, TC Acoustic, Home Nursing Foundation, and Pontiac Land Group.

Four of the five small and medium entrants were new, with Merz Aesthetics the only carry-over in that category. JustCo, TC Acoustic, Home Nursing Foundation, and Pontiac Land Group appeared on the Asia list through Singapore alone, rather than through multiple national markets. That makes their inclusion more locally specific than the listings of large multinationals that qualify through operations across several countries.

Why the ranking matters

Workplace lists can be easy to dismiss as employer branding exercises, especially in a region where companies often use awards to signal stability to customers, partners, and prospective hires. The Great Place To Work methodology, however, is built around employee responses rather than a judging panel.

Employees answer 60 statements on a five-point scale, along with two open-ended questions. The survey covers issues such as whether leaders are accessible and honest, whether pay and promotion are seen as fair, and whether employees feel their work has meaning.

A key part of the ranking is consistency. Great Place To Work measures how much responses vary within the same organisation. A company where one department is highly engaged but another is disengaged will be marked down compared with one that produces more even scores across teams and job functions.

This is particularly relevant in Southeast Asia, where companies often manage large differences between headquarters staff, frontline workers, technical teams, regional offices, and outsourced functions. A polished culture at the management level does not necessarily translate into trust across the organisation.

Also Read: The work culture paradigm in a hybrid-first world

This year’s Asia list drew on surveys of companies employing more than 8.9 million people, with more than 3.8 million individual employee responses across 36 countries and territories.

For Singapore, the result reinforces its position as a regional hub for talent-intensive industries, from enterprise technology and payments to biotech, hospitality, real estate, and manufacturing. But the wider labour market signals are less comfortable. With skilled-worker turnover projected to remain high and pay increases trailing the regional average, employers will need more than brand recognition to keep people.

In a market where employees have options, the companies that stand out may be those that can make fairness and trust feel consistent, not occasional.

The post Singapore tops Southeast Asia on Best Workplaces in Asia 2026 list appeared first on e27.

Beyond Border, the Singapore-founded, tech-enabled US immigration platform for founders and highly skilled professionals, has added former Airbnb China COO Kum Hong Siew to its advisory bench as Business Advisor.

The appointment comes as the company, which says it has stayed net profitable for two years while hitting a multimillion-dollar annual revenue run rate, pushes further into the US and prepares to expand into Latin America.

Also Read: Late-stage deals revive in Southeast Asia, but early-stage founders remain under pressure

Siew’s résumé reads like a masterclass in scaling across regulatory complexity: one of Airbnb’s earliest Asia Pacific hires, he went on to run the company’s regional business before becoming COO of Airbnb China, a market notorious for grinding down foreign operators. Before that, he cut his teeth at law firm Rajah & Tann and at Yahoo!.

e27 sat down with Siew (and on two questions specifically about Beyond Border’s market strategy, with CEO and co-founder Fred Ng) to unpack what “operational scaling” actually means for an immigration platform, why the O-1 visa’s “Einstein” reputation is mostly myth, and what a law-and-tech background taught him about vetting founders before agreeing to advise them.

You went from Airbnb to immigration tech. What drew you to Beyond Border specifically, and what parallels do you see between scaling Airbnb’s regional operations and helping founders navigate US immigration?

Kum Hong Siew: What drew me in was the founders first. Fred Ng and Arnold Ip are strong founders who are building something special. Silicon Valley is the indisputable epicentre of the AI revolution, and ambitious founders and builders around the world increasingly understand that they need to move there to have truly global impact.

I also see the mission in broader terms than a typical visa-processing pitch. When someone moves to the US and succeeds, that success flows back home too, especially true for folks hailing from emerging markets like Latin America.

The Airbnb connection, though, is about something more specific than mission alignment for me. When it involves cross-border movement of people, local nuance and understanding is critical. And it can be challenging to maintain this local mindset as you scale a business. I learnt this from my time at Airbnb, and I look forward to helping Fred and Arnold do this as well.

You went from one of Airbnb’s earliest APAC hires to COO of Airbnb China, a market known for regulatory complexity. What lessons from operating there apply to Beyond Border’s move into Latin America?

Kum Hong Siew: I’m careful not to overclaim a one-size-fits-all playbook here. Every country really is different. But there are some fundamental truths in running and scaling a business. For example, succeeding in China requires a relentless focus on the customer, and that translates everywhere else.

Beyond Border frames immigration as sitting “at the intersection of legal, operational and business strategy.” Can you unpack that with a concrete example? How does a founder’s visa status actually shape decisions like where to hire or raise capital?

Kum Hong Siew: Most of Beyond Border’s customers have already made the harder decision before they ever speak to us. They’ve already decided that they want to be in the US, usually because that’s where funding, talent and customer demand are frequently the deepest and densest in the world. And then Beyond Border helps them get the right visa to be there.

Physical presence isn’t optional if a founder actually wants to build a team or raise capital in the US. It’s very hard to do those things successfully if you are constantly flying in for two weeks before leaving, and coming back only in a few months’ time.

Also Read: Why the smartest founders are interviewing investors before investors interview them

I’ll offer a data point from my own angel investing. I have a portfolio founder from Hong Kong who now spends more than half their time in San Francisco. Since they started doing this late last year, they’ve more than 4x-ed their annualised revenue.

As Business Advisor, you’ll work on legal operations and operational scaling. What does “operational scaling” mean for an immigration platform — internal processes, attorney networks, client experience, or all three?

Kum Hong Siew: I’ll be blunt about the industry’s starting point. Immigration service providers have historically been heavy on manual processes and operations. With AI, there is now an opportunity to reinvent how such services can be delivered, to enable faster, cheaper, and higher-quality outcomes.

But I push back on treating AI as a bolt-on. It’s not about taking an existing workflow and automating select steps with AI, while essentially doing the same steps in fundamentally the same way. Instead, it’s about designing and building a completely new workflow from first principles using the technology that is now possible, while maintaining or even improving the quality of the deliverables.

My bet is on combining Beyond Border’s talented workforce and attorney networks with technology to serve far more clients without diluting the experience.

You’ve worked across law, tech, and now immigration. How has that cross-disciplinary background shaped how you evaluate a company like Beyond Border before agreeing to advise it?

Kum Hong Siew: My background allowed me to quickly understand the problem they were solving, and the different ways in which they could attack it as they scale. What sealed the decision for me was less the numbers than the founders themselves. They have already built a good business, but want to make it even bigger and better. They had a really good understanding of their business and the levers for further growth, and were also open and hungry to learn. At this stage, everything starts with and flows from the founders. So what I saw made the decision easy.

Beyond Border has grown to a multimillion-dollar revenue run rate while staying net profitable for two years. What told you this was a fundamentally sound business rather than just a fast-growing one?

Kum Hong Siew: Those metrics, together with my understanding of how such businesses operate, show that Beyond Border could grow to be a great business at scale.

Singapore-founded startups often face the question of when and how to internationalise. What do you make of Beyond Border’s decision to headquarter in Singapore while its core product serves the US market?

Fred Ng: I’d correct the framing a little there. Beyond Border was founded in Singapore, but today we are headquartered across the US and Hong Kong. Singapore remains an important part of our story, and Asia continues to be a core market for us.

I see our geographic spread as a philosophy rather than an accident. One of our core beliefs is that where you come from should not define where you can go. Singapore gave us a strong launchpad, with its concentration of talent and its position as a regional business hub, but the company has grown organically into an increasingly international business.

Also Read: Connecting founders across SEA used to be the easy part of the job, and now it’s becoming the whole job

The numbers back that up: more than 60 per cent of enquiries now come from the US, with the rest split across Asia, Europe and Latin America. For us, internationalisation has therefore been less about choosing between Singapore, Asia or the US, and more about building the company in the markets where our customers are today and where they want to go next. I think it also shows that a consumer company founded in Singapore can build from Asia and find meaningful traction with a global audience.

A press release cites that immigrants co-founded 59 per cent of America’s billion-dollar startups. From your vantage point, what’s the biggest misconception people have about who actually needs O-1 or EB-1A pathways?

Fred Ng: I’ll go straight for the myth that keeps eligible applicants from even trying. The biggest misconception is that you have to be “Einstein-smart” to qualify for an O-1 or EB-1A. These pathways have developed a reputation as the “Einstein visas,” which leads many founders and professionals to assume they are out of reach.

I’d point to approval data as the counter-argument. O-1 approval rates, for instance, have remained above 90 per cent even across both Trump administrations. What matters is whether you can demonstrate a strong track record of professional achievement and recognition within your field. As a rule of thumb, if you have achieved meaningful professional milestones that are recognised by your industry, it is worth assessing your eligibility rather than ruling yourself out because of the “extraordinary ability” label.

Beyond Border is expanding into Latin America alongside its US and Asia footprint. What operational or cultural challenges do you anticipate in building trust with founders and professionals in a market quite different from Southeast Asia?

Kum Hong Siew: I’ll return to a theme I raised earlier: there’s no universal playbook, only a universal starting point. I always advise founders to start with the customer. When you do that, you can use first principles to figure out the best way to build trust with and to serve the customer. The way to do that will vary across countries and cultures, hence local nuance and understanding is critical. That is why Beyond Border’s obsession with being customer-centric has served and will continue to serve them well.

The post Why Beyond Border thinks visas are now part of the founder playbook appeared first on e27.

The AI community keeps asking: “Where’s crypto’s killer app?” They may be asking the wrong species. Blockchain never found product-market fit with humans. It may find it with machines. AI creates intelligence. Blockchain creates verification.

For years, one of the strongest arguments from the AI community has been that blockchain never found a meaningful product-market fit. While artificial intelligence is transforming software development, healthcare, education and enterprise productivity, much of the crypto industry continues to be associated with speculative trading, meme coins and short-lived narratives. From this perspective, blockchain appears to solve few problems that traditional software cannot already address. Ironically, however, the rapid rise of AI agents may become the strongest argument yet for why blockchain infrastructure matters.

The key distinction is that humans and AI agents operate under completely different economic constraints. Most people already have access to bank accounts, credit cards, digital wallets and payment processors such as Visa, Stripe or PayPal. AI agents have none of these privileges. They cannot open bank accounts, complete Know Your Customer (KYC) verification, own payment cards or initiate cross-border bank transfers as legal entities. Yet they are increasingly expected to purchase APIs, rent GPU resources, subscribe to SaaS products, hire freelancers, negotiate cloud computing prices and interact autonomously with other software agents. Intelligence alone is insufficient; autonomous systems also require programmable identity, ownership and payments.

Also Read: EVs gain ground in the Philippines as fuel costs squeeze drivers

Blockchain provides precisely these missing primitives. A wallet serves as a native financial account for software, stablecoins enable instant global settlement without relying on banking hours, smart contracts automate payment conditions, decentralised identity establishes verifiable credentials, and tokenised assets create programmable ownership. Instead of replacing AI, blockchain increasingly functions as the financial infrastructure layer that allows autonomous agents to participate in the digital economy.

“AI makes everything fake. Crypto makes things real again,” said Balaji Srinivasan.

This transition is already visible across the industry. Coinbase recently introduced x402, an open protocol that enables AI agents to pay for APIs automatically using stablecoins by reviving HTTP status code 402 (“Payment Required”). Visa launched Intelligent Commerce, allowing AI assistants to make purchases within predefined spending limits, while Mastercard introduced Agent Pay to support secure AI-driven transactions. Stripe has expanded programmable stablecoin payment infrastructure, and startups such as Skyfire and Payman AI are building dedicated financial rails for autonomous software agents. None of these initiatives focus on retail crypto speculation; instead, they address a practical question: how can software become an economic participant?

This shift also changes the way blockchain should be evaluated. During the previous cycle, most discussions revolved around transaction throughput, token prices and decentralised finance yields. In the AI era, the more relevant question may be whether blockchain can become the operating system for machine-to-machine commerce. Autonomous vehicles paying charging stations, AI researchers purchasing proprietary datasets, robots ordering replacement components, and software agents negotiating cloud infrastructure all require trusted payments, programmable ownership and verifiable identity. These are infrastructure problems rather than consumer applications.

“Crypto startups shouldn’t pivot to AI. Crypto is foundational infrastructure for AI,” said Brian Armstrong, Coinbase.

The debate therefore may have been framed incorrectly from the beginning. Artificial intelligence and blockchain solve fundamentally different problems. AI produces intelligence by enabling machines to reason, generate content and make decisions. Blockchain produces trust by enabling ownership, payments and coordination without centralised intermediaries. Rather than competing for the same market, the two technologies increasingly appear to be complementary layers of the same digital economy. The next decade is unlikely to be defined by AI versus blockchain. It is far more likely to be defined by AI powered by blockchain, where intelligence and trust evolve together to support an economy increasingly driven not only by humans, but also by autonomous machines.

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