We may be using AI to save hours of work, only to fill those hours with more work.

This is something I have been thinking about as organisations accelerate their investment in AI.

The productivity opportunity is undeniable. Generative AI can draft in seconds, analyse large volumes of information, automate administrative work and give leaders access to capabilities that previously required significantly more time and resources.

A 2025 field experiment involving more than 7,000 knowledge workers across 66 companies found that workers who actively used generative AI spent roughly two fewer hours each week on email and reduced work outside normal hours.

Yet look at what is happening alongside these gains. In Singapore, 58 per cent of leaders said productivity needed to increase, while 81 per cent of the workforce reported lacking sufficient time or energy to do their work. Globally, Microsoft workplace data found that during core working hours, employees can be interrupted by meetings, emails or messages as frequently as every two minutes.

Something does not quite add up.

We are giving people tools that allow them to work faster, while many of those same people are telling us they are running out of time and energy.

This is why I believe we need to separate two conversations that are often treated as one.

Productivity is not the same as human capacity.

We are increasing output, but are we increasing capacity?

Productivity asks how much we can produce.

Human capacity asks how much complexity, pressure, change and decision making a person can sustainably hold while continuing to think, lead and perform well.

AI can significantly expand the first. Leadership needs to take responsibility for the second.

Consider what happens when an executive saves five hours each week through AI. If those five hours are immediately replaced by additional meetings, decisions, targets and deliverables, have we improved the way that executive works?

Or have we simply made it possible for them to carry more?

This distinction matters because human beings have cognitive, emotional and attentional limits. Under sustained pressure, our judgement changes. We can become more reactive. We listen differently. Strategic thinking becomes harder because our attention is consumed by what is immediately in front of us.

The concern I have is that organisations could become significantly more productive through AI while the people inside them become increasingly depleted.

That is not sustainable transformation.

Human performance needs to become part of AI strategy

Human performance beyond productivity deserves its own attention.

Can a leader access their best judgement when the stakes are high? Can they maintain clarity when information is coming from every direction? Can they regulate their own response to pressure while creating confidence and stability for the people around them?

These questions become more important as AI accelerates the speed at which leaders receive information and are expected to act on it.

Also Read: AI and inflation are driving Singapore’s workplace anxiety. Employers are spending on the wrong fix

Senior leaders are already navigating transformation, geopolitical uncertainty, changing workforce expectations, stakeholder pressure and difficult questions about where human judgement should sit alongside machine intelligence.

Giving those leaders more information does not automatically help them lead better.

A tired executive can still answer another email and attend another meeting. The more important question is whether that executive still has the capacity to notice what others have missed, challenge an assumption, listen properly to resistance from the organisation or make a difficult decision with clarity.

This is why I believe human performance belongs inside the AI investment conversation, alongside technology, productivity and return on investment.

Reinvest the capacity AI gives back to people

We need to stop measuring the value of AI only by how much additional work people can produce.

The 2025 field experiment gives us an interesting example. AI users reduced the time they spent on email, but there was no significant reduction in meetings.

Technology changed part of the individual’s workload, but it did not automatically change the organisation around them.

That requires leadership.

If AI gives an executive team back 10 or 20 per cent of its time, organisations have a choice about what happens to that capacity. Some of it can certainly support greater productivity, but some should be reinvested into strategic thinking, customer conversations, developing people, experimentation and simply having enough space to think before another important decision is made.

For me, this is a much more interesting measure of AI return on investment.

It is not only about getting more output from people. It is about making better use of their human capability.

Also Read: Singapore’s next AI industry may be the people who check the agents

Build human capacity alongside AI capability

The second opportunity is to deliberately develop the human skills required to perform in an AI enabled environment.

We need leaders who know how to manage their attention, not simply their calendars. Leaders who can remain grounded under pressure, separate urgency from importance and question an AI recommendation rather than accepting it because the analysis looks convincing.

We also need leaders who can create enough psychological safety for their teams to experiment, make mistakes, challenge assumptions and admit when they do not know the answer.

Most importantly, we need to move away from the idea that peak performance means operating at maximum intensity indefinitely.

Sustainable peak performance is the ability to access greater capacity when the situation requires it and to know how to recover that capacity afterwards.

The strongest leader in an AI enabled organisation may therefore not be the person capable of carrying the greatest workload. It may be the person who can retain clarity, judgement, trust, energy and connection as the speed and complexity around them increases.

What will we do with the capacity AI gives us?

AI gives organisations extraordinary access to intelligence and efficiency. I am optimistic about what that can create.

But as intelligence becomes more accessible, our deeply human capabilities become even more important: judgement, discernment, creativity, courage, trust, presence and connection.

The organisations that thrive will understand that AI capability and human performance need to grow together.

As the founder of XfinitySG, a leadership and human performance practice, I believe this is where the next phase of the AI conversation needs to go. We have spent considerable time asking how quickly organisations can adopt AI and how much productivity it can unlock.

Now we need to ask what happens to the humans working inside those faster organisations.

Technological capability can scale rapidly. Human capacity needs to be intentionally developed, protected and expanded.

So, as AI gives your organisation the ability to do more, perhaps the more important question for leaders is:

What will you do with the human capacity AI gives back to you?

—

The post AI can increase productivity. It can’t increase human capacity appeared first on e27.

Most Singapore office workers know the ritual. A message lands at 10.47pm. It is not urgent. You answer it within 90 seconds anyway, not because the work demands it, but because you want someone to notice that you answered it within 90 seconds.

It turns out that performance is playing to a near-empty theatre.

The Jobstreet by SEEK Singapore Workplace Culture Study 2026, conducted by Milieu Insight among 600 employees and jobseekers and 200 hiring decision-makers, finds that bosses are mostly not watching the clock. They say they want initiative and outcomes. Workers, meanwhile, are busy proving something else entirely.

Also Read: When global uncertainty becomes workplace anxiety

The numbers make the mismatch stark. Some 49 per cent of employers say taking initiative and delivering outcomes is what they value most; only 32 per cent of employees say that is how they demonstrate commitment. Instead, 63 per cent of employees lean on responding quickly, a behaviour just 39 per cent of employers prize. And 52 per cent of workers signal dedication through availability outside working hours, which only 18 per cent of employers actually rate.

In plain terms, Singapore’s workforce is optimising hard for a KPI its managers barely track.

The anxiety is imported

Why would rational people exhaust themselves chasing the wrong metric? The report’s answer is that workplace stress has stopped being homegrown. It is less about the difficult manager or the overloaded sprint, and more about forces arriving from outside the office: the inflation print and the AI roadmap.

Both sides name the same culprits. AI and automation are cited as drivers of workplace change by 56 per cent of employees and 53 per cent of employers. Economic uncertainty, inflation and cost of living follow at 53 per cent and 46 per cent respectively.

Where they part ways is in what that fear means. Employees’ top concern is salary growth that keeps pace with living costs (60 per cent), followed by job security (48 per cent), work-life balance (37 per cent) and rising competition for roles (33 per cent). Employers, by contrast, list the impact of AI on workflows and teams (52 per cent), managing salary expectations amid rising costs (49 per cent) and restructuring towards leaner teams (24 per cent).

Read those two lists side by side and a feedback loop appears. When leadership answers volatility by tightening budgets and accelerating automation, it feeds the job-security fears that push staff into hyper-visibility. Being permanently reachable becomes a cheap insurance policy against becoming the next line on a restructuring spreadsheet.

This is not a Singapore-only phenomenon. Across Southeast Asia, the post-funding-winter instinct to run lean, AI-first teams instead of hiring has made “do more with fewer people” the default operating mantra, while AI is already set to redesign how companies work, not merely which jobs survive.

An expensive habit with no payout

The always-on norm is now mainstream. Some 63 per cent of employees feel the need to be always-on almost every day (32 per cent) or a few times a week (31 per cent). Employers are not innocent bystanders: 85 per cent say their organisation expects staff to be available somewhat often (62 per cent) or often (23 per cent). Tellingly, 55 per cent of employees and 45 per cent of employers call it a shared responsibility.

Also Read: Burnout isn’t just personal, it’s becoming an operations problem

The cost is real. Among those under always-on pressure, 63 per cent report lower energy, 47 per cent report reduced job satisfaction and 22 per cent call the culture unsustainable because of burnout. Some 43 per cent rely on taking leave simply to cope, and 9 per cent say they lack the time or energy to recover at all. Annual leave, in other words, has become a repair shop rather than a holiday.

The return is close to nil. Some 62 per cent say always-on working has made no difference to their career progression, and 59 per cent see no difference in recognition. On work quality, 52 per cent see no change and 18 per cent report a decline; only 30 per cent report an improvement. Just 6 per cent believe being always-on is the main route to advancement.

If a fund manager pitched that risk-return profile, no LP in Singapore would sign the cheque.

The bill eventually lands on the balance sheet. Across the wider workforce, 36 per cent of employees are actively exploring new opportunities. Among those feeling always-on pressure, 10 per cent are actively looking for an exit, 11 per cent want out but feel unable to leave, and 14 per cent have disengaged to doing only what their job description requires. Founders who still treat this as a soft HR issue should look at the case for building burnout-resistant teams as an operating discipline.

Employers know. They are spending elsewhere

Here is the uncomfortable part. Some 81 per cent of employers who expect always-on behaviour acknowledge its long-term downside, chiefly burnout (66 per cent) and disengagement or turnover (15 per cent). Yet 48 per cent have no plans to introduce related support policies.

The budget tells the same story. AI and digital skills training (34 per cent), technology tools (25 per cent), flexible work arrangements (23 per cent) and upskilling (22 per cent) top the investment list. Wellbeing and mental health (14 per cent), workload management (13 per cent), manager development (12 per cent) and extra hiring to ease pressure (12 per cent) trail behind.

Even the support that exists is largely invisible. Half of employees say they receive no organisational support at all, against just 22 per cent of employers who admit to offering none. Workload conversations are offered by 40 per cent of employers but recognised by only 22 per cent of employees; boundary protections, 36 per cent versus 19 per cent; confidential feedback channels, 33 per cent versus 13 per cent. Support that lives only on the HR intranet is, functionally, not support.

The irony is hard to miss in a city that tops Southeast Asia on the Best Workplaces in Asia 2026 list. Neighbours are improvising their own coping mechanisms: in the Philippines, workers are already using AI as a burnout shield.

A caveat worth stating: this is a Singapore-only sample of 800 respondents, employer answers are self-reported, and a jobs platform has an obvious interest in workers being restless. Still, the direction of the gaps is too consistent to wave away.

What would actually change behaviour

The report proposes three shifts.

• First, move from availability to outcomes: define performance around milestones and business impact, and say explicitly that a fast reply at midnight is not a commitment metric.
• Second, move from flexibility to sustainable work design, with regular workload reviews, clear asynchronous norms and a shared definition of what is genuinely urgent.
• Third, move from support on paper to support in practice, by training line managers to hold honest conversations about workload, pay and restructuring.

Also Read: The AI layoff trap points straight at Southeast Asia

The sharpest point concerns AI. Upskilling will not calm anyone if staff suspect it is a prelude to headcount cuts. It has to be tied visibly to internal mobility and career pathways, especially as AI begins to reshape entry-level tech roles.

Singapore’s Tripartite Guidelines on Flexible Work Arrangement Requests, in force since December 2024, cover how staff ask for flexibility, not when they may switch off. Until employers draw that line themselves, the 10.47pm reply will keep doing what the data says it does: costing workers dearly and earning them almost nothing.

The post AI and inflation are driving Singapore’s workplace anxiety. Employers are spending on the wrong fix appeared first on e27.

Google has been busy making friends in Singapore. On 7 October, its AI Futures Fund teamed up with Granite Asia to write cheques of up to US$2 million into Asian AI startups. Eight days later, the same fund has walked into Sea Limited’s office carrying something different: not a term sheet, but a key to Google’s model cupboard.

Sea, the Singapore-founded parent of Shopee, Garena and Monee, announced on Thursday that Sea Labs, its technology innovation and development arm, will work directly with Google AI Futures Fund to explore and build new applied AI products.

Teams across Sea will get access to selected frontier models from Google, along with Google experts who can help develop and test Sea’s new product ideas.

Also Read: Granite Asia, Google AI Futures Fund team up to back Asia’s AI founders

Jonathan Silber, co-founder and Director of Google AI Futures Fund, called Sea Labs the fund’s “first partnership with an external AI lab”. It is the most interesting line in the announcement. It is also the one that raises the most questions.

What is actually being traded

The AI Futures Fund sits inside Google Labs and usually backs startups with a bundle of early access to Google DeepMind models, capital, distribution and mentorship. Sea is not a startup. It is a NYSE-listed company that booked US$22.9 billion in revenue in 2025. It does not need Google’s cheque, and the announcement does not mention one.

No investment amount, equity stake, compute commitment, exclusivity terms, timeline or product milestones were disclosed. The currency here is access, and it flows both ways.

Google gets what every model maker craves: a live testbed of hundreds of millions of daily users across Southeast Asia and Latin America, shopping, gaming and borrowing in dozens of languages, on budget phones and patchy networks. Sea gets hands-on time with Google’s most advanced models, plus engineers who can help it decide what is worth building on top of them.

The logic holds. At the API level, frontier models are steadily becoming interchangeable. What separates winners is how fast a model turns into a product people use every day, and few companies in the region ship consumer products at Sea’s scale.

Chapter two, with chapter one still unread

The tie-up builds on the expanded strategic partnership the two signed in February 2026, which covered gaming, agentic commerce and agentic payments. Back then, Shopee and Google said they would explore an AI shopping agent spanning both platforms, while Monee agreed to give feedback on Google’s Agent Payments Protocol (AP2).

Also Read: Sea’s blockbuster 2025 masks a tightening profitability squeeze

Thursday’s announcement offers no update on how those February pilots have fared. Instead, it adds a new layer on top. For a company that wants investors to see it as an AI story, that is a little like announcing a sequel before anyone has watched the first film.

Forrest Li, Sea’s Chairman and CEO, framed the move as part of a bigger shift. “Our ambition to become an AI-native company goes beyond improving what we do today,” he said, adding that some of the most meaningful applications of AI “are still to be discovered”.

The R&D question

That ambition sits a little awkwardly next to Sea’s own numbers. Its research and development expenses fell 7.8 per cent in the fourth quarter of 2025 to US$277.6 million, even as group revenue rose 38.4 per cent. Shopee’s operating margin in that quarter was roughly 2.5 per cent.

Read that way, borrowing a frontier lab’s brainpower is a sensible move for a thin-margin commerce business. It buys frontier capability without frontier capex. The announcement does point to Sea’s own in-house AI stack, including proprietary models and accelerated compute infrastructure, so this is not a company starting from scratch.

The risk is dependency. Every prototype Sea’s teams build on Google’s models makes switching later more expensive. Founders in the Granite Asia programme were explicitly promised full technical choice across their AI stack. Sea’s announcement makes no such statement.

Everyone has a model patron now

Sea is hardly alone in pairing up with a big AI player. Grab, its closest regional rival, has worked with OpenAI since 2024 and last week signed a framework agreement with SoftBank and PETROS to explore an AI infrastructure platform in Sarawak. In Indonesia, GoTo co-developed the Sahabat-AI language model with Indosat Ooredoo Hutchison.

In the region’s e-commerce fight, Lazada draws on parent Alibaba’s Qwen models, and TikTok Shop sits on ByteDance’s in-house AI. Every major Southeast Asian platform now has a foundation-model patron. Sea’s choice of Google fits Google’s own regional playbook of scaling Gemini through schools, telcos and local languages.

The difference is depth. Most of these tie-ups are supplier relationships. Google calling Sea Labs a fellow “lab” suggests it wants something closer to co-development, with Sea’s markets as the proving ground.

What to watch

The announcement promises products tailored to local contexts and needs. In practice, that means multilingual customer service, tools for small sellers, and fraud detection for first-time digital borrowers. These are areas where Southeast Asia’s messiness is a genuine advantage for whoever learns from it first.

Also Read: Vietnam’s shoppers are asking AI first, but Google still gets the last word

But the question raised in February remains unanswered. When an AI agent becomes the storefront, whose interest does it optimise: the buyer’s, the seller’s, or the platform’s take rate? A lab partnership with no disclosed guardrails does not settle it.

For now, “two labs” building together at the frontier makes for a good soundbite. The real test is whether, by the next 12.12 sale, a seller in Surabaya or a gamer in São Paulo notices anything different.

The post Sea and Google open chapter two of their AI pact before chapter one ships appeared first on e27.

In August 2024, Google wrote Character.ai a cheque for roughly US$2.7 billion. On Monday, Disney offered US$20 million. Both were for a licence to the same company’s technology. Allowing for the fine print, that is a fall of more than 99 per cent in a little over two years.

Character.ai filed for Chapter 11 bankruptcy protection in the US on 12 October, naming Disney as the stalking-horse bidder for some of its intellectual property. The company blamed legal, regulatory and operational headwinds. The court filing puts estimated assets and liabilities at US$50 million to US$100 million each. If no buyer wants to keep the platform running, it will be wound down.

Also Read: AI governance is moving from promises to proof

It is tempting to read this as an American story about an American lawsuit culture. It is not. It is a preview of what happens when a business sells intimacy to the people least equipped to handle it. Southeast Asia (young, mobile-first and lightly regulated on this front) should be reading the filing very carefully.

A licence, a rehire, then a shell

Start with how Character.ai got here. The 2024 Google deal was officially a technology licence. Its real prize, as The Wall Street Journal reported, was bringing co-founder Noam Shazeer back to Google, where he went on to help lead Gemini. Shazeer reportedly made hundreds of millions of dollars from his stake, an unusually large payout for a founder who never sold his company or took it public.

The talent left. The company stayed behind, and so did its users, its moderation problems and its lawsuits.

Those lawsuits came from families who alleged the platform contributed to their teenagers’ self-harm and deaths, among them the Florida mother of a 14-year-old boy. Character.ai and Google agreed to settle five cases, including hers, earlier this year. Kentucky’s attorney general has separately sued the company, alleging that the platform exposed children to dangerous responses.

The irony writes itself. A year ago, Disney sent Character.ai a cease-and-desist letter over bots impersonating Elsa, Moana and Darth Vader. Disney then hired former Character.ai chief executive Karandeep Anand as its first chief technology officer. Now it is bidding for the leftovers.

Nobody in this chain did badly except the company itself and, far more painfully, the families. That is the structure worth naming: the reverse acqui-hire, where the buyer licenses the technology and hires the people, and the liabilities stay with a shell that has to keep the lights on.

Engagement was the product. That was the problem

The deeper failure is the business model. A companion app makes money from attachment. The more hours a user spends, the better the metrics look. The users most willing to give those hours are often young, lonely or both.

That creates a trap no product manager can design their way out of. Every safety feature cuts into the engagement the business runs on. Character.ai added pop-ups pointing users to mental-health resources. In April, it widened age restrictions to more regions and capped some features. Each change was right. Each one also made the numbers worse, while compute bills and legal costs kept arriving and ChatGPT and Gemini offered the same conversations for free inside products people already used.

Also Read: The EU called ChatGPT a search engine. SEA’s AI startups should worry about what comes next

So the lesson is not that Character.ai moderated too little, or too late, though critics will argue both. It is that in companion AI, duty of care is not a feature. It is the biggest line on the cost sheet, and a business that cannot afford it does not have a business.

Southeast Asia’s rulebook has a chatbot-shaped hole

To its credit, Southeast Asia has moved faster than most on children online. Indonesia’s PP Tunas regulation took effect on 28 March, forcing high-risk platforms to deactivate accounts belonging to under-16s. The named platforms include YouTube, TikTok, Facebook, Instagram, Roblox and Bigo Live. TikTok alone has deactivated nine million underage accounts. Malaysia’s Online Safety Act came into force in January, and since June, social media platforms must verify the ages of new users with a MyKad, passport or digital ID.

Notice what those lists have in common. They are social and gaming platforms. A 15-year-old in Surabaya locked out of Instagram can still spend her evenings confiding in a chatbot that remembers everything she says and is engineered never to get bored of her. The bot is arguably the more intimate product. It is also the one the rules barely name.

Other governments have spotted the gap. Canada’s proposed Safe Social Media Act brings AI chatbots under the same child-safety regulator as social platforms, with a legal duty to act responsibly. The UK has pledged to bar under-18s from romantic and sexual AI companions. Southeast Asian regulators have already shown they can act fast on AI harms: Indonesia, Malaysia and the Philippines all blocked Grok this year over non-consensual sexualised deepfakes. Extending child-protection regimes explicitly to companion AI, rather than waiting for a test case, is not a stretch.

A note to founders building the ‘AI best friend’

The region has no shortage of founders pitching companion and wellness bots that speak Tagalog, Bahasa or Vietnamese, understand homesick migrant workers and fill gaps left by thin mental-health services. Some of that need is real. But the pitch deck’s best line, that users are lonely and the app is always there, is also its biggest liability.

Three things follow from Character.ai’s filing. First, budget for duty of care from day one: age assurance, crisis escalation and human review cost money, and they are the product, not an add-on. Second, stop treating time-in-app as the north star. A companion that keeps a vulnerable teenager talking until 3am is not succeeding.

Third, do not assume an exit will carry the risk away. Character.ai’s investors got their liquidity through Google; the liabilities stayed exactly where they were.

Investors have homework too. Due diligence on a companion startup should ask about harm, not just retention curves. If a fund cannot get a straight answer on how the product handles a user in crisis, that is the answer.

Don’t wait for the bankruptcy court

Character.ai’s founders were paid. Google got its talent. Disney may get a bargain. The families got settlements, and nothing that settles what they lost.

Also Read: Why Southeast Asia’s next healthtech winners will be built around healthcare workflows, not just AI

Southeast Asia does not have to learn this lesson the American way, one lawsuit at a time. Its regulators have already accepted that platforms owe children a duty of care. Its founders still have time to build companion products that earn trust rather than harvest attention. The question is whether anyone acts before the region produces its own case file.

The post Character.ai was worth US$2.7B to Google. Disney just offered US$20M appeared first on e27.

Indonesian fintech expansion into ASEAN looks different in 2026 than it did when Investree opened its Thailand operations in 2018 and its Philippines unit shortly after. The cohort of Indonesian platforms that pushed beyond the domestic market between 2018 and 2022 (Investree, Modalku, several payment players, a handful of insurtechs) encountered a structural cost that the original expansion theses tended to underestimate.

Some platforms have scaled regionally. Several have pulled back. Investree, whose Indonesian licence was revoked by OJK in October 2024, is one of the more prominent examples of a platform whose domestic situation eventually reshaped its regional footprint.

After 15 years inside Indonesian risk functions, including a period reporting to a foreign parent inside an Indonesian banking subsidiary, I have watched the cross-border fintech ambition with more interest than most. The structural pattern underneath the regional expansion outcomes is more useful than the company-by-company narrative.

What Indonesian fintech expansion aimed to do

The thesis driving Indonesian fintech expansion into ASEAN between 2018 and 2022 was straightforward. Indonesia had built fintech infrastructure (credit underwriting models, alternative data pipelines, distribution channels, regulator-engaged operating practices) that was, in principle, transferable to other ASEAN markets with similar structural characteristics. The implicit assumption was that the cost of expansion would be additive but the underlying playbook would translate.

What turned out to be harder

Three structural factors made the actual cost of cross-border expansion higher than original models priced in.

Regulatory archipelago. ASEAN’s financial regulators are coordinating more than they used to, but they are not converging. Indonesia’s OJK, Thailand’s Bank of Thailand and SEC, Bangko Sentral ng Pilipinas, the Monetary Authority of Singapore: each operates a distinct licensing regime, capital requirement, reporting framework, and supervisory culture. A platform expanding into three ASEAN markets is effectively running three different regulatory institutions inside one company. The compliance cost is multiplicative, not additive.

Also Read: Fintech, DeFi and applied AI define Southeast Asia’s new venture discipline

Local credit physics. The credit risk profile an Indonesian platform underwrote successfully at home did not always translate cleanly. Thai SME borrowers behave differently from Indonesian SME borrowers. Filipino consumer credit operates inside a different demographic and macro context. Models calibrated on Indonesian data systematically misfit other markets, and the recalibration took longer and cost more than expansion budgets typically provided for.

Operational gravity. Senior Indonesian fintech talent did not relocate easily to regional offices. Local hires required a different management cadence than the founder-led culture that had worked in Jakarta. Customer acquisition costs were higher because brand recognition was lower. Each operational decision required either expensive local presence or expensive remote management.

Lessons learned

Six principles emerge from the cross-border ambition cycle.

Expansion is not a discount. The unit economics of running an Indonesian fintech regionally are worse than running the same operation domestically until scale is reached. Platforms that priced expansion as a growth multiplier without pricing the compliance and operational drag underestimated how much capital the journey would consume before it returned.

Regulatory engagement compounds locally, not regionally. A platform’s reputation with OJK does not transfer to Bank of Thailand or Bangko Sentral ng Pilipinas. Each regulator relationship has to be built locally, over years, with local presence and local engagement.

Credit models are local. Alternative data underwriting models trained on Indonesian borrowers misfit other ASEAN borrowers in ways not always visible at deployment. Cross-border platforms that treat their models as transferable without explicit recalibration accumulate hidden default risk.

Also Read: The Philippines doesn’t need more fintech apps; needs rails

Local partnership matters more than ownership share. The cross-border platforms that worked best entered through serious local partnerships with banks, distributors, or operators who already had regulatory standing and customer trust. Going alone with a Jakarta-trained team and an Indonesian playbook produced consistent underperformance.

Domestic stability is a precondition. Regional expansion accelerates when the home market is operating cleanly. When the home market faces stress (regulatory action, credit cycle pressure, governance challenges), the regional footprint becomes a liability that consumes management attention and capital exactly when domestic operations need both. Investree’s experience makes this clearest: regional positions cannot insulate against domestic regulatory consequences.

The ASEAN fintech franchise is not yet a thing. Despite a decade of expansion attempts, no Indonesian fintech has built a genuinely scaled regional franchise in the way Grab built one in ride-hailing or Sea Group built one in gaming. Whether the model is wrong or the time has been too short, the data so far suggests the regional fintech franchise is harder than the regional consumer-tech franchise.

The macro stakes

ASEAN’s fintech landscape will eventually have regional champions. The structural opportunity (fragmented incumbent banking, mobile-first populations, integrating regulators, growing SME credit demand) is real and not going away. But the path will be slower, more capital-intensive, and more dependent on local partnership than the first wave attempted to walk.

For Indonesian fintechs still considering regional expansion in 2026 and beyond, the question is no longer whether the opportunity exists. It is whether the platform has built the domestic foundation, the partnership infrastructure, and the capital depth required to pay the cross-border tax that the first generation revealed.

—

The post Why Indonesian fintech’s ASEAN expansion cost more than anyone priced in appeared first on e27.

Singapore’s war on scams has mostly been fought far from the victim: in police operations centres, bank back offices and, more recently, in rulebooks telling platforms what they must block. Now the city-state wants part of that fight to happen inside the phone itself.

Google on Wednesday announced two initiatives with the Singapore Government, unveiled on the sidelines of Singapore International Cyber Week (SICW) 2026. The first brings Singapore into Google’s Fairwind Programme, a gated scheme that gives vetted defenders the company’s most capable cyber models, including its new frontier model, Gemini 4 Argon.

The second pairs GovTech Singapore with Google DeepMind to build an on-device AI model for Android that flags scam attempts in real time without sending private conversations to the cloud.

Also Read: Singapore’s cybersecurity paradox: Leading in digital, lagging in defense

Both deals are framed as defensive. Both also deepen Singapore’s reliance on a single US tech giant at a time when access to frontier AI has become a geopolitical lever.

An unguarded model for trusted hands

Fairwind launched on September 2 as a limited access programme for governments and trusted partners, starting with Gemini 3.8 Flash Cyber paired with Google’s CodeMender agent. Argon, announced on September 30, has since become the programme’s headline act. Fairwind partners and internal Google teams get a version of Argon without its cyber guardrails, the filters that normally block requests that look like hacking.

Organisations may only grant Argon access to internal cybersecurity, incident response, or penetration testing teams, and must track employee access and use.

Google’s pitch is speed. Instead of spending weeks manually fixing vulnerabilities, it says defenders can generate verified, deployment-ready patches in minutes inside their own secure cloud environment. On Google’s own leaderboard, Grok 4.7, Gemini 4 Argon and GPT-6 Astra tie at 68 per cent on the CWE-bench v1 remediation benchmark, which also means the model still misses roughly a third of the test’s tasks.

“Minutes, not weeks” is a vendor claim, not a measured outcome. Analysts have pointed out that the clearest evidence would be merged-patch or time-to-remediation results from Fairwind partners, which Google has not published. An AI that finds bugs faster than humans can review fixes risks lengthening the backlog, not shortening it.

There is also the question of who sees what. Google says it monitors Argon’s chain of thought and its actions and stops execution when necessary. That is a sensible safety measure, though it implies a foreign company has some line of sight into how a government’s defenders probe their own systems. Google does say Argon supports zero data retention when used as a managed model on Gemini Enterprise, but the terms of Singapore’s participation were not disclosed.

A crowded, fragile club

Google is not the only lab rationing its sharpest cyber tools. Anthropic launched Project Glasswing on April 7, 2026, deploying its restricted Claude Mythos Preview model to a vetted consortium, and OpenAI responded with Daybreak on May 11. Each programme decides which governments and companies qualify, making access itself the product.

That access can disappear overnight. In June, US export controls forced Anthropic to suspend its Mythos and Fable models before Washington lifted the restrictions at the end of the month, an episode e27 has argued created a hydra rather than containing risk. For a small state building critical defences on American models, the lesson is clear: the off switch sits in another capital.

The scam problem is a human problem

The second initiative targets a more personal threat. Singapore’s scam-related losses fell 19 per cent to S$913.1 million (about US$715 million) in 2025, but the composition of those losses explains why the government is looking at devices. In 81.8 per cent of 2025 scam cases, victims transferred the money themselves rather than having their accounts taken over. Takedowns and bank freezes mostly come too late; the decisive moment is when a manipulated person presses “send”.

Also Read: Deepfake fraud losses hit US$3.7B as scams spread beyond social media

The GovTech-DeepMind model is designed for that moment. Lightweight Gemma models are fine-tuned on local scam narratives, including impersonation of public institutions, banks and local figures, and run entirely on the handset. When a conversation shows the high-pressure patterns of social engineering, a warning pops up on screen.

“Fighting scams shouldn’t mean sacrificing personal privacy,” said Mark Chen, Director of Trust and Safety at GovTech Singapore, adding that the approach keeps personal communications completely private.

The announcement leaves key questions unanswered. There is no launch date, no accuracy or false-positive data, and no explanation of how the model reads conversations inside end-to-end encrypted apps like WhatsApp or Telegram, which by design hide content from everyone but the sender and recipient. The model is also Android-only in a market where iPhones are hugely popular, leaving a large share of users uncovered.

The timing is not accidental. Singapore has tightened scam rules for messaging, social media and e-commerce platforms, with Google Messages and Google Meet among the designated services that must comply by January 31, 2027, under threat of fines of up to S$10 million (US$7.83 million). Meta has already disrupted 3.7 million scam-linked assets alongside police, and authorities have taken down 30,000 iMessage accounts. A government-endorsed on-device shield is as much Google’s compliance story as its public-safety one.

It also enters a crowded field. Singapore’s ScamShield app already filters calls and messages, and caller-ID players such as Taiwan’s Gogolook (Whoscall) and Truecaller have built large regional followings. Google’s edge is distribution: a model built into Android reaches people who would never download a separate app.

Grading its own homework

Google surrounded the announcement with supporting statistics. It says Google Play Protect has blocked more than 3.3 million installation attempts of potentially malicious apps across more than 770,000 devices, without specifying the period. It also cites studies showing that ten minutes of its Be Scam Ready game reduced players’ estimated scam risk by 20 per cent, and is launching a 2.0 version that halves playtime. These figures come from Google, and the effects of a quiz on behaviour months later are hard to measure. Its earlier work with the region, such as the US$5 million ASEAN Foundation drive, followed a similar mix of philanthropy and product.

Also Read: Should cybersecurity be nationalised?

If the on-device model works, the stakes reach well beyond Singapore. Malaysia ranked highest in the region for scam prevalence, with 32 per cent of adults losing money to scams, followed by the Philippines at 31 per cent. In those markets, as e27 has argued on Thailand’s scam epidemic, the problem is now technological and so must be the response, especially as AI phishing turns trust into the weakest link.

Singapore has once again cast itself as the region’s test bed. Whether this test produces a public shield or a deeper dependency will depend on details neither side has shared yet.

The post Singapore wants Google’s AI to catch scammers before victims hit send appeared first on e27.

In early 2018, Yujun Chean and Prateek Jogani were selling car insurance with a cashback twist. Their Singapore startup, Vouch Insurtech, had just raised SGD1 million (then US$755,000) in seed funding as the parent of Thai insurtech FairDee. Eight years on, the pair are back with a much bigger cheque and a far more crowded market.

Their new company, Parallel Loop, has launched with US$6.6 million in seed funding led by Bengaluru-based 3one4 Capital. Picus Capital, Flourish Ventures and Economic Development Innovations Singapore (EDIS) also joined, alongside three high-profile angels: Paytm founder and CEO Vijay Shekhar Sharma, CRED founder Kunal Shah, and Philip Yeo, former chairman of Singapore’s Economic Development Board and A*STAR.

Also Read: Everyone is talking about AI adoption. But are enterprises actually ready for it?

Founded only in June 2026, Parallel Loop calls itself an “AI operating partner” for mid-market and large enterprises. Four months in, it has 30 staff across Singapore, Bengaluru, Bangkok, London and Dubai, and more than 10 clients. These include Indian meat-delivery firm Licious, electricals maker Havells and Tokio Marine Safety Insurance (Thailand). Chean told e27 that Singapore enterprises, including a local healthcare group, are also on the list.

“Our aim is to build a global AI company,” he said.

The gap it is chasing

The pitch rests on a problem most enterprise leaders will recognise. AI tools have made individual employees faster, but turning that into a company-wide capability is far harder. The rules, definitions and know-how that make a business tick are scattered across systems, meetings, inboxes and chat apps. A model that cannot see that context is a clever intern who skipped onboarding.

“Intelligence has become abundant. What many organisations lack is the context, systems and capability to turn this into tangible business outcomes,” said Jogani.

The diagnosis fits the region. Southeast Asia, the argument increasingly goes, does not have an AI adoption problem so much as a scaling problem, and most enterprise AI in APAC is still stuck in the proof-of-concept room.

Three products, one bet

Parallel Loop’s offering has three parts:

• AI Enablement works with leadership teams to identify high-value use cases and build roadmaps.
• The AI Stack is a modular platform that connects a client’s existing systems with its organisational context. It powers everything from live dashboards to deterministic workflows, meaning automations that produce the same result every time rather than the varied output typical of generative models.
• The Software Factory pairs forward-deployed engineers with AI agents to modernise legacy systems and build new applications. Those forward-deployed engineers are the centre of gravity. Popularised by Palantir, the model embeds a vendor’s engineers inside client teams instead of handing over software and walking away.

The stack runs in the client’s own cloud or on premises, with data residency set by the client. It works with multiple model providers, including open-weight models, and keeps an audit trail of prompts, responses, context changes and actions. Clients retain ownership of their data and of the work built for them. That speaks directly to the regional appetite for sovereign AI, where, as one recent analysis argued, sovereignty starts long before the model itself.

Also Read: Why Southeast Asia cannot build sovereign AI on borrowed choices

Licious is the showcase client. Co-founder Abhay Hanjura said the company set out this year to become “fully AI-native”. He said teams across every function now use Parallel Loop daily as “a shared intelligence layer across the organisation”.

A familiar founding duo

The founders’ track record goes a long way towards explaining why investors moved so quickly. Jogani and Chean have worked together for nearly a decade. After Qoala acquired FairDee in 2021, Jogani became Group CTO and Chean Group COO of the Indonesian insurtech.

Qoala grew into five markets, processing millions of policies a month, and raised more than US$130 million from investors, including a US$65 million Series B in 2022. Jogani, who brings around two decades of technology experience, began running hands-on AI programmes for leadership teams in 2025. He noticed that many teams were adopting AI tools, but few were building the systems and processes needed to sustain the gains.

“They’re closing enterprise deals and expanding accounts across multiple geographies with an outcome-led approach,” said Anurag Ramdasan, Partner at 3one4 Capital.

A crowded field

Parallel Loop is walking into a brawl. Global consultancies such as Accenture and Deloitte sell AI transformation programmes to the same boardrooms. AI labs, including OpenAI, have built forward-deployed engineering teams of their own to win enterprise accounts, and Palantir still sets the template. Closer to home, Singtel’s RE:AI has partnered the SIT-NVIDIA centre to tackle the enterprise AI deployment gap.

The model also carries a familiar risk. Forward-deployed engineers are expensive, and the more a startup leans on them, the more it resembles a consultancy: software margins on the pitch deck, services margins on the books. Thirty people serving more than 10 clients across five cities in four months is fast. Whether the AI Stack becomes a reusable product, or each engagement turns bespoke, will decide whether Parallel Loop is valued as a software company or a services firm.

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

There is a quieter tension, too. A stack that promises no model lock-in also makes it easier for clients to walk away once the capability is built in-house.

For now, the founders have the network, the cheque and a client list that spans three countries. The harder test is whether enterprises that have been told to stop automating broken processes and calling it AI transformation will pay someone to fix the process first.

The post Parallel Loop nets US$6.6M to sell enterprises the one thing AI models lack: context appeared first on e27.

An AI chatbot that drafts a bad email is an embarrassment. An AI agent that sends it, to the wrong customer, with the wrong file attached, after approving the wrong refund, is a liability. Singapore’s regulators have clearly been losing sleep over the second scenario.

On Wednesday, the Infocomm Media Development Authority (IMDA) announced three initiatives at the AI Assurance Exchange: The Agentic Edition, an event organised by the AI Verify Foundation (AIVF) and SGTech that drew more than 300 deployers and testers. They are a starter kit for testing agentic AI systems, an accreditation programme for third-party AI testers with five firms in its first batch, and a new taskforce to defend the infocomm and media sector against AI-enabled cyber threats.

Also Read: What AI safety researchers actually worry about

Individually, each is a modest piece of guidance. Together, they signal something bigger: Singapore is moving from telling companies how AI governance should work to building an industry whose job is to prove that it does.

Why agents need a different exam

Traditional software follows rules. Generative AI produces content. Agentic AI does something more unnerving: it reasons, acts on a user’s behalf, touches sensitive data and changes the systems around it. Testing only the final output, as most companies do with chatbots, misses most of what can go wrong.

The Starter Kit for Testing Agentic Systems for Safety and Reliability tries to fill that gap with a four-step approach: identify risks such as erroneous or unauthorised actions, design realistic tests without causing real-world damage, run the tests and analyse the logs, then monitor and retest continuously.

The more interesting part is the new vocabulary. The kit asks testers to examine an agent’s entire “trajectory”, meaning both its reasoning and its actions, and to track agent-specific metrics. Tool use accuracy measures whether the agent called the right tool. Handoff accuracy checks whether it passed the task to the right agent or human. Human-in-the-loop rates show how often a person had to step in. It also tackles a problem most enterprises will recognise: how to test a system when you have only black-box visibility, because you bought the agent from a vendor rather than built it.

The kit draws on consultations with government agencies and more than 20 deployers and testers, including Google, AWS and Resaro, and includes case studies from Cohere, CyberSierra, Earlybird AI, Thailand’s KBTG and Knovel Engineering. It extends Singapore’s Model AI Governance Framework for Agentic AI, released earlier this year, and an earlier starter kit for testing large language model apps. IMDA calls it a living document, which is a polite way of admitting that nobody has fully worked this out yet.

The demand is real. In August, a Sumsub and Singapore Fintech Association study found that 94 per cent of Singapore businesses were using or piloting multi-step AI systems, yet only 29 per cent could produce an audit trail for AI-driven decisions. Ninety-eight per cent said they were ready to adopt third-party verification. The market for checking agents exists. The question was who would do the checking.

A badge for the testers

AIVF’s answer is the AI Tester Accreditation Programme (AI TAP), which it bills as a first-of-its-kind scheme for third-party AI testing providers. Applicants go through technical, operational and financial evaluations covering testing competency, track record, delivery capability and financial sustainability. That last criterion is telling. Regulators clearly don’t want an assurance firm folding halfway through an engagement.

Also Read: From copilots to colleagues: How agentic AI is redefining enterprise productivity

The first five accredited firms are AIDX Tech, Knovel Engineering, PwC Singapore, Resaro and Vulcan. Accreditation lasts one year, so firms have to keep earning it as the technology changes.

The stakes go well beyond enterprise IT. AIVF says the testers will assess applications in healthcare, finance, hiring and public services, the areas where an unchecked agent can quietly reject a job applicant, deny a claim or misroute a citizen’s request without anyone noticing.

Vulcan co-founder Alex Leung put the case bluntly: when AI can “send emails, move data or approve transactions, testing isn’t optional.” Resaro co-CEO April Chin argued that organisations need evidence from testing done outside the development team, not just from the people who built the system.

The questions the badge doesn’t answer

The programme deserves some scrutiny. The release does not say whether any sector regulator will require accredited testing. Without that, AI TAP risks becoming a marketing badge rather than a safeguard.

There is also a small-world problem. Resaro helped inform the starter kit, and Knovel Engineering appears in its case studies. Both are now accredited testers. In a market this young, that overlap is probably unavoidable and not improper. But financial auditing offers a clear lesson about what happens when the firms that write the standards, sell the advice and certify the results are the same few names. PwC’s presence in the first cohort makes the comparison hard to ignore.

Cost matters too. Large banks can afford independent AI audits. A Series A startup in Jakarta or Ho Chi Minh City running customer-facing agents may not, which leaves the free starter kit as its realistic option. Singapore has long pitched its governance toolkits as templates for ASEAN neighbours, and Malaysia is preparing an AI Governance Bill. Whether AI TAP travels across the region will depend on whether it stays affordable.

A taskforce on a 2027 deadline

The third initiative, the Frontier AI Resilience and Security Taskforce (FAIRS), brings together more than 20 leaders from telecommunications, media, cybersecurity and AI. Its focus is the risk that frontier models are getting better at finding vulnerabilities in networks that businesses and government systems depend on.

It has three priorities: faster response to AI-accelerated threats, segmenting critical networks to stop disruption from spreading, and pooling sector-wide intelligence on new attack techniques. Its first report is due in the first half of 2027. Attackers armed with the same models, already behind a wave of deepfake fraud, are unlikely to wait that long.

Also Read: Nearly half of APAC enterprises commit US$1M+ to agentic AI

Still, the direction is right. As e27 has argued, AI governance is moving from promises to proof. Singapore has now built the exam, chosen the first examiners and set a renewal date. Whether anyone is required to sit the test is the next policy fight.

The post Singapore’s next AI industry may be the people who check the agents appeared first on e27.

Anthropic has finally given its Singapore customers somewhere to knock. The San Francisco-based AI lab opened an office in the city-state on Wednesday, its fifth in the Asia Pacific, and signed a memorandum of understanding (MOU) with the Ministry of Digital Development and Information (MDDI) that puts AI safety testing at the centre of the relationship.

The MOU was signed on Tuesday, on the sidelines of Singapore International Cyber Week, by MDDI Permanent Secretary Chng Kai Fong and Anthropic co-founder Ben Mann, with Minister for Digital Development and Information Josephine Teo and Anthropic’s Head of APAC Policy Michael Sellitto looking on. It covers three areas: AI safety, security and model evaluation; research on how AI is adopted and what it does to the economy; and responsible adoption across government and industry.

Also Read: Singapore wants Google’s AI to catch scammers before victims hit send

The timing is hardly accidental. The same week, the Infocomm Media Development Authority (IMDA) unveiled new assurance measures for AI agents, a sign that Singapore’s regulators now worry more about software that acts than software that chats.

What is actually in the deal

Strip away the diplomatic language, and the MOU commits Anthropic to technical exchanges with Singapore’s AI safety, security and assurance institutions on model evaluation, testing standards and incident response, the playbook for what happens when a model misbehaves in the wild. Anthropic also says it will share research and data from its Anthropic Economic Index, help small and medium-sized enterprises (SMEs) adopt AI, build AI skills among public officers, and offer policy input as Singapore drafts its AI rules.

None of this is new territory. Anthropic joined the Generative AI Evaluation Sandbox run by IMDA and the AI Verify Foundation in 2023, and later worked with them on red-teaming (deliberately attacking a model to expose its weaknesses) in English, Mandarin, Malay and Tamil. The MOU formalises a relationship that already existed.

What the announcement leaves out is just as telling. There is no investment figure, no headcount target for the office beyond a note that it is hiring, and no word on whether the SME and skills programmes come with money attached. MOUs are statements of intent, not contracts, and Singapore signs plenty of them.

The customer list is the real story

Anthropic’s strongest case for an office here is the business it already has. More than 1,500 engineers and technical staff at the Government Technology Agency of Singapore (GovTech Singapore) use Claude Code, its AI coding tool, to modernise government systems. The Cyber Security Agency of Singapore is exploring how Claude could support the country’s cyber defence.

In finance, DBS uses Claude for software development and productivity in its Singapore operations, while sovereign wealth fund GIC is rolling it out across investment and corporate services teams. Regulated firms can now run Claude with in-region inference on Amazon Bedrock, meaning their data is processed within the region. That matters now that the Monetary Authority of Singapore has given financial firms a year to prepare for AI risk rules.

Also Read: Why Southeast Asia cannot build sovereign AI on borrowed choices

The consumer internet giants are in too. Grab says BriX, its Claude-powered sales assistant, saves its sales teams around 40,000 hours a quarter. Sea uses Claude to make Shopee’s customer service chatbot faster and more accurate, and thousands of its employees write and debug code with it. Supabase, the developer platform with Singaporean roots, is building with Claude Code.

A caveat: these figures come from Anthropic and its customers, not independent audits. So does the claim that Singapore ranks second of 121 countries for Claude.ai usage relative to population, at nearly six times what its size would predict. Usage per head also says little about value created, a gap visible in research showing messy data keeps many SME pilots stuck.

“Our customers here hold AI to an extremely high standard, especially when it comes to reliability and security,” said Dale Finlay, Anthropic’s General Manager for ASEAN.

Rivals are circling the same prize

Anthropic is not alone. OpenAI last month hired former Elastic and Snowflake executive Sanjay Deshmukh to lead Asia Pacific sales, and has backed a public-private AI accelerator in Thailand. Google is betting on schools, telcos and local languages to scale Gemini across the region. Databricks has unveiled a US$350 million investment plan for the city-state. And Singapore’s own SEA-LION model offers agencies a homegrown option for Southeast Asian languages.

What sets Anthropic’s pitch apart is that it leads with safety evaluation rather than consumer reach, the area where Singapore, through AI Verify, has made itself a global reference point. For a lab whose brand rests on pairing capability with safety, a government that tests models for a living is a natural partner.

The questions an MOU cannot answer

Two uncomfortable issues sit beneath the handshake photos.

The first is dependency. In June, Anthropic suspended access to its Fable and Mythos models for nearly three weeks to comply with US Department of Commerce export controls, restoring it on 1 July. That episode was a reminder that Washington, not Singapore, holds the off switch for America’s frontier AI. The MOU says nothing about that risk, and it is fair to ask how GovTech would cope if a similar order hit the models its engineers now rely on.

The second is commercial. Anthropic’s IPO prospectus shows two customers generated nearly a quarter of its 2025 revenue, while the company plans to spend US$518 billion on compute and infrastructure. Spreading revenue beyond a few big US clients is a necessity, and a market of wealthy, AI-hungry institutions is where that starts. The office is a safety partnership, but it is also a sales operation.

Also Read: Singapore’s AI dividend will depend on what happens after the pilot phase

Neither point makes this a bad deal for Singapore. Closer access to a frontier lab’s evaluation methods strengthens its claim to be the region’s AI assurance hub, and local developers, more than 300 of whom attended Anthropic’s Builder Summit this week, gain a shorter line to the company.

The real test will come later, in what the MOU produces: published testing standards, incident-response protocols that get used, and SME programmes with funding behind them. Until then, Singapore has a new tenant with an impressive customer list, and another piece of paper.

The post Anthropic’s Singapore MOU bets on safety testing, not consumer reach appeared first on e27.

Anyone who has spent 10 minutes pressing “0” to escape a voice bot knows how the last automation wave ended. Call centres were promised dramatic savings from IVRs and chatbots. Customers got phone trees that went in circles, and companies got a fresh layer of complaints to manage.

Sunny Rao sold that promise from the inside. Before becoming SVP of APAC at JFrog, he spent years at Avaya, Genesys and Nuance, three firms that defined the contact-centre automation era. Now he is watching Southeast Asia fall for AI agents, and he thinks the region risks repeating an old mistake: confusing a faster task with a more productive business.

Also Read: Singaporean founders’ Lightsage bags US$4M to decode how AI agents choose software

“An agent completing a task faster does not necessarily translate into greater productivity, particularly if it creates additional review, remediation or security work downstream,” he tells e27 in an email interview.

The adoption-readiness gap

The enthusiasm is not in doubt. A recent Agoda AI Developer Report found that 53 per cent of developers in the region say agents are already in production or broad use. Only 38 per cent believe their codebase is ready for that autonomy, a readiness gap e27 has examined before, and one that explains why regional tech leaders are learning to trust agents, but not with production.

Rao, who speaks regularly with CIOs in Singapore, Jakarta and Bangkok, frames the problem as one of control rather than capability. Coding agents now select dependencies, pull tools, modify code and interact with enterprise systems at machine speed. A company may know an agent is running without knowing what it consumed, which permissions it used or whether its actions complied with policy.

“The real readiness test is not whether an enterprise can deploy autonomous agents, but whether it can detect and control unintended actions before they become business risks,” he says. The worry has only sharpened since OpenAI shelved GPT-6.1 Astra, a model built to handle complex tasks with less human supervision.

Tokens are the wrong yardstick

Most companies still measure AI spend in seats or tokens. Rao thinks both miss the point. If a CFO could track one number, he would pick cost per successful, production-ready outcome.

The logic is simple arithmetic. An agent that is cheap to run but needs repeated prompting, produces unusable work or creates hours of human clean-up may cost more than a pricier one that gets it right first time. Tokens and licences are inputs, not value, a point that echoes e27‘s look at the hidden economics of autonomous AI agents.

Also Read: Agentic AI ambitions in Singapore run into legacy systems and data quality gaps

Take a pull request an agent writes in two minutes that a senior engineer then spends 45 minutes fixing. For software teams, Rao says, the better measure is Mean Time to Release, how long code takes to reach production, weighed against the company’s overall security posture. Code that introduces vulnerabilities or regressions can delay a release rather than speed it up.

In regulated industries, checks once run after deployment are becoming mandatory release gates, and two-minute code that cannot clear them is worth little.

The blind spot: what agents eat

Ask Rao where APAC enterprises are most exposed, and he points not at access but at consumption. Access is increasingly treated as an identity and permissions problem, which security teams broadly understand. Less visible is what agents pull in to finish a job: open-source packages, MCP servers (connectors built on the Model Context Protocol that plug agents into tools and data), skills and plugins. Any of these can carry vulnerabilities or malicious code into enterprise infrastructure before anyone notices.

“The critical control point is where an agent consumes a component,” he says.

When that control fails, the blame does not get diluted. If an agent pulls a compromised MCP server into production at a fintech regulated by the Monetary Authority of Singapore, Rao says the institution deploying it remains accountable, not the model provider or the governance vendor. That means being able to reconstruct which agent acted, under whose authority, what it consumed and why it was allowed. Many firms cannot do that yet: a Sumsub study found Singapore companies’ AI audit trails remain thin.

The vendor in the room

There is an obvious catch. JFrog sells exactly the controls Rao describes, through its MCP Registry, AI Catalog and Agent Guard. “Agents are risky, so buy our governance” is a convenient story for a vendor to tell.

Rao’s defence is that headcount no longer tracks risk. A 30-person startup running agents may operate workflows once associated with a far larger engineering team. If it builds for a bank or insurer, it inherits that client’s security and regulatory demands regardless of size. He concedes startups do not need bank-grade architecture, but argues the minimum, verifying what agents consume, limiting permissions and keeping evidence of how software was built and approved, becomes a commercial requirement long before any breach.

Pricing is harder to pin down. JFrog does not publish list prices for AI Catalog or Agent Guard; Rao says they are integrated capabilities within the JFrog Platform, not standalone products. That may be so, but it leaves buyers calculating governance ROI without a visible line item, an awkward gap for a company urging CFOs to measure cost per outcome.

JFrog is not alone in this race. Sonatype and Snyk have long pitched software supply chain security, and Snyk bought agent-security startup Invariant Labs in 2025. GitHub and Docker run their own MCP catalogues inside platforms developers already live in. For Southeast Asian teams, the question is less whether to govern agents than whose platform gets to hold the keys.

Singapore’s next exam

Rao expects Singapore to get agent governance right first, citing its regulatory track record and concentration of multinationals in regulated sectors. JFrog’s own 2026 Software Supply Chain State of the Union research found 71 per cent of Singapore respondents carefully review AI-suggested security fixes, the highest of eight markets surveyed.

Also Read: SEA’s insurers face a new question: what happens when customers have agents?

The same data shows the cracks. Some 18 per cent have policies against unauthorised AI tools but no way to detect violations, and 59 per cent wait a week or more for open-source package approvals. That is fertile ground for Shadow AI, employees quietly bringing in tools without IT’s knowledge, which Rao says worries organisations more than any rush to hand agents critical decisions. The one call he would keep with humans: which sensitive systems an agent may touch, and how far its permissions stretch.

The lesson from the IVR years still holds. Automation that merely made old processes cheaper disappointed; automation that rethought them paid off. Singapore’s next test, Rao says, is turning governance rules into automated enforcement inside developer workflows. Until then, the agent that writes code in two minutes may still be the slowest route to production.

The post Tokens are the wrong yardstick for AI agents, says JFrog’s Sunny Rao appeared first on e27.