LinqAlpha, a New York-headquartered AI startup building intelligence tools for institutional investors, has raised US$22 million in a Series A round backed by a group of global and Asian investors.

The round was anchored by AVP, Atinum Investment, and GFT Ventures. It also drew participation from financial institutions and venture platforms across Singapore, Hong Kong, South Korea, Japan, and India, including SBI Investment, Z Venture Capital, Betatron Venture Group, East Ventures, SV Investment, Samsung Securities, Mirae Asset Venture Investment, Mirae Asset Capital, NH Investment & Securities, Shinhan Venture Investment, Hana Ventures, and NuVentures.

Also Read: From energy to ergonomics: 20 AI startups to watch in Southeast Asia

The company said the capital will be used to expand its global team, deepen integrations with market and alternative datasets, and accelerate deployment of its multi-agent platform across equities, macro, credit, and multi-asset strategies.

Founded by Jacob Choi, Subeen Pang, Jin Kim, and Hojun Choi, LinqAlpha brings together former Goldman Sachs analysts and MIT computer science PhDs. The startup says its platform is already used by more than 70 financial institutions across the US, Europe, and Asia, including sell-side sales, trading, and research teams at investment banks, as well as buy-side firms such as Causeway Capital Management and Schonfeld Strategic Advisors. Its buy-side clients collectively manage more than US$5 trillion in assets.

From search to synthesis

LinqAlpha is entering a market where institutional investors are drowning in data but still struggling to convert information into judgment quickly enough.

For public-market investors, the problem is no longer access. Earnings calls, regulatory filings, broker notes, central-bank commentary, shipping data, satellite imagery, social-media chatter, supply-chain signals, and credit-market movements are all available in some form. The harder task is linking these signals before they become obvious to everyone else.

That challenge is particularly acute in Asia, where market-moving events can cut across geographies within hours. A semiconductor supply-chain disruption in Taiwan or South Korea, a policy decision in China, a currency move in Japan, or a change in commodity demand from Southeast Asia can quickly influence equities, credit, macro trades, and sector views globally.

“The first wave of AI in finance made analysts faster. The next wave changes what they can know,” said Hojun Choi, co-founder and co-CEO of LinqAlpha. “The edge no longer comes from retrieving information; it comes from systems that surface market-moving signals before they are priced in.”

LinqAlpha’s pitch is that generic AI assistants are not enough for professional investors. Instead, the company allows institutional teams to deploy specialised AI agents that learn their investment frameworks, thesis history, research preferences, and feedback loops. These agents are designed to synthesise internal research with external market data, rather than simply retrieve documents or summarise information.

Jacob Choi, co-founder and co-CEO of LinqAlpha, described the product as “a second brain for every investment team”, built to turn accumulated research into actionable insight across liquid public markets.

Why Asian capital is paying attention

The investor syndicate is notable for its Asian depth. Beyond the lead investors, the round includes names from Japan, Korea, India, Hong Kong, and Southeast Asia. East Ventures, one of Southeast Asia’s most active early-stage investors, and Betatron Venture Group, a Hong Kong-based venture platform, are among the backers. SV Investment also brings links across Korea and Southeast Asia.

Also Read: In the age of AI, people matter more than ever

This matters because Asia is both a market opportunity and a stress test for products such as LinqAlpha. The region has complex cross-border capital flows, a fragmented data environment, multilingual markets, and a growing base of institutional investors that need to track global and regional signals simultaneously.

Singapore, in particular, has become a key hub for asset managers, hedge funds, family offices, and private banks. According to the Monetary Authority of Singapore, assets under management in the city-state stood at roughly US$4 trillion in 2023, making it one of Asia’s most important capital-allocation centres. The government has also been pushing the use of AI and data in financial services through regulatory sandboxes, AI governance frameworks, and industry collaborations.

Across Southeast Asia, the broader financial-services sector is also becoming more data-heavy. Digital banks, brokerages, wealth platforms, and institutional trading desks are generating and consuming more real-time information. While much of the region’s fintech investment over the past decade went into payments, lending, and consumer finance, AI infrastructure for financial institutions is now becoming a more serious category.

That shift is being driven by two forces: pressure on financial firms to improve productivity, and the need for differentiated insight in markets where information is increasingly commoditised.

A crowded race in financial AI

LinqAlpha is not alone in chasing this opportunity. The financial information market is already dominated by incumbents such as Bloomberg, LSEG, FactSet, S&P Global, and Moody’s, all of which are embedding generative AI into terminals, data products, and research workflows.

Bloomberg has developed finance-specific AI models and has been adding AI-powered search and summarisation to its ecosystem. FactSet and LSEG are also integrating large language models into analytics and workstation products. S&P Global has Kensho, which applies AI to financial and business data.

On the startup side, companies such as AlphaSense, Hebbia, Rogo, Dataminr, and RavenPack are competing across market intelligence, document search, alternative data, and financial research automation. AlphaSense, for example, has grown into a major player in AI-powered market intelligence by aggregating broker research, transcripts, filings, and expert-call content. Hebbia targets financial and legal professionals with AI agents for complex document analysis. Dataminr and RavenPack focus on real-time event detection and alternative data signals.

The competitive question for LinqAlpha is whether it can move beyond summarisation and search into persistent, investor-specific reasoning. Many AI tools can now condense a transcript or retrieve a filing. Fewer can adapt to how a portfolio manager or analyst builds conviction, tracks prior theses, and weighs contradictory market signals over time.

Manish Agarwal, General Partner at AVP, said LinqAlpha is targeting “a larger opportunity” than retrieval or automation: helping institutional investors discover differentiated insights in markets that reward speed, context, and proprietary judgment.

Execution will decide the outcome

For all the excitement around AI agents, the bar in institutional finance is high. Tools must be accurate, auditable, secure, and deeply integrated into existing workflows. Hallucinations, weak sourcing, or poor data lineage can be costly in investment decision-making. Financial institutions also tend to move slowly when adopting new systems, especially those touching proprietary research and trading processes.

LinqAlpha’s early traction with banks and buy-side firms gives it a stronger starting point than many AI startups selling into finance. But scaling across regions and asset classes will require more than model quality. The company will need robust data partnerships, enterprise-grade controls, and enough customisation to serve different investment styles without becoming a services-heavy business.

Also Read: AI is a game-changer, and here’s how your business can use it to win

The new funding gives LinqAlpha room to build that infrastructure. It also reflects a broader conviction among Asian investors that the next generation of financial technology will not simply digitise transactions, but reshape how capital-market professionals think, research, and act.

For Southeast Asia, where Singapore is increasingly competing as a global asset-management and AI hub, LinqAlpha’s round is another sign that institutional finance AI is becoming a serious investment theme. The winners will be those that can turn fragmented market noise into decision-ready intelligence before the rest of the market catches up.

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Treasurer, an AI financial technology company, used Echelon Singapore 2026 to introduce AlphaLenz, its enterprise AX solution for financial and insurance institutions, as it looks to deepen its footprint across Southeast Asia’s fast-growing fintech and enterprise AI market.

The company presented AlphaLenz at the two-day event, held from June 3 to 4 at the Suntec Singapore Convention & Exhibition Centre, as part of the Seoul Business Agency (SBA) Pavilion. The platform was shown to investors, fintech firms, financial institutions, insurers and enterprise partners, with Treasurer positioning it as a practical AI tool built for the realities of financial workflows rather than a broad, general-purpose assistant.

Built for financial data, not just general AI tasks

AlphaLenz draws on Treasurer’s core strengths in AI financial analytics, market data processing and financial intelligence automation. The solution began as a tool to support investment research and financial data analysis, but is now being expanded into an enterprise AX platform aimed at helping financial and insurance organisations automate repetitive tasks, speed up information processing and reduce the burden on teams handling large volumes of documentation.

Also Read: 5 ways generative AI is transforming the payments ecosystem

For banks, insurers and other financial enterprises, the challenge is rarely a shortage of data. The problem is making sense of it quickly. These firms handle market information, corporate disclosures, product and policy documents, customer-facing materials, internal reports, risk-related records and regulatory information, often across multiple systems and formats.

That means staff spend significant time on manual work such as collecting data, reviewing documents, summarising information, comparing records and preparing reports. Treasurer says AlphaLenz is designed to ease these processes.

Tackling repetitive workflows in finance and insurance

AlphaLenz combines AI agents, structured financial data workflows and industry-specific automation capabilities to support a range of enterprise use cases. These include financial data analysis, document review, market monitoring, report generation, product and policy information structuring, and internal knowledge search.

By automating repetitive analytical tasks, the platform is intended to free analysts, managers and back-office teams to focus on higher-value decision-making. That positioning is likely to resonate in a region where financial firms are under pressure to improve productivity while keeping headcount and operational costs in check.

Treasurer argues that this makes AlphaLenz different from off-the-shelf AI tools. Rather than being built for generic productivity, it has been designed around financial data structures and workflows specific to the sector.

Also Read: AI’s transformative role: Making insurance accessible and affordable globally

Its underlying technology is powered by Treasurer’s financial analytics engine, which processes Korean and Asian market data, company information, disclosures and industry trends. That foundation, the company says, allows AlphaLenz to deliver more relevant automation for enterprises operating in finance, insurance and capital markets.

Strong interest from Southeast Asia’s enterprise AI market

At Echelon Singapore 2026, Treasurer met with investors, fintech executives, financial institutions, insurance stakeholders and innovation teams. The company said it found strong interest in AI-driven workflow automation, cost-efficient financial data processing and enterprise AI adoption among organisations seeking to boost productivity with limited resources.

The reception highlights a broader trend across Southeast Asia, where financial institutions are increasingly exploring AI not just for customer service or front-end digital experiences, but for the less visible work that keeps operations moving: document handling, information retrieval, internal knowledge management and compliance-related processing.

For many firms, that back-office layer remains a major cost centre. Tools like AlphaLenz are being pitched as a way to streamline those functions without requiring organisations to rebuild their entire technology stack.

Treasurer eyes regional expansion

“Echelon Singapore 2026 was a meaningful opportunity for us to introduce AlphaLenz not only as an AI financial analytics platform, but as an enterprise AX solution for financial and insurance institutions,” said Ted Kim, CEO of Treasurer. “Financial companies are under increasing pressure to process more data, generate insights faster, and improve productivity with limited resources. AlphaLenz is designed to help these organisations automate repetitive workflows and make better use of their internal and external data.”

He added that Treasurer’s strength lies in its ability to structure complex financial information into actionable intelligence.

“Treasurer’s strength lies in AI-based financial analysis technology and the ability to structure complex financial data into actionable intelligence. By applying these capabilities to enterprise AX solutions, we aim to help financial and insurance companies improve work efficiency, reduce resource costs, and accelerate digital transformation across their organisations,” Kim said.

Treasurer joined the event through the SBA Pavilion, a programme aimed at helping promising Seoul-based startups expand into global markets. The company took part in exhibition showcases, demo presentations, private pitching sessions and AI-powered business matching activities, creating opportunities to connect with potential investors, partners and enterprise customers in Singapore and the wider region.

Next steps for AlphaLenz

Following the event, Treasurer plans to continue discussions with financial institutions, insurance companies, fintech firms and enterprise partners in Singapore and Southeast Asia. The company also intends to further develop AlphaLenz’s data coverage, AI agent capabilities and industry-specific workflows as it works towards broader adoption.

Also Read: AI agents are already inside your systems, but who’s controlling them?

The move comes as enterprises across the region increasingly look for AI systems that can do more than generate text or answer simple queries. In finance and insurance especially, the real value lies in tools that can read, compare, organise and act on large and fragmented data sets with speed and consistency.

For Treasurer, AlphaLenz represents a bet that the next wave of enterprise AI in Southeast Asia will be less about flashy demos and more about practical automation. In that sense, the platform’s pitch is straightforward: less manual drudgery, more informed decision-making, and a smoother path towards digital transformation.

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As FitnessForce folds into US-based Daxko, the more interesting story is not the deal itself but what happens after it. Can a company built around the messy realities of India and the Gulf keep its product edge inside a larger US software group?

In this edited Q&A with e27, Hadi Curtay, former CEO of FitnessForce and now Managing Director at Daxko, argues that the company’s value was never about hype. It was built around localisation, operational detail, and software for fitness operators running across multiple sites and markets. He says India and the GCC remain the company’s strongest territories, while Southeast Asia is promising but uneven.

Also Read: The rise of ‘Strava Jockeys’: How Indonesia’s vanity economy is hacking the fitness tech ecosystem

Curtay also addresses a familiar founder anxiety after acquisitions: the team stays, the knowledge gets transferred, and the product slowly fades into somebody else’s roadmap.

The fundraising journey in emerging markets can be rough. Did regional VCs really understand the problem you were solving?

Not really, and that shaped how we built FitnessForce. Most investors understood the broad market story. It was not hard to see that fitness in India, the Middle East, and Southeast Asia was growing on the back of rising incomes, urbanisation, and greater health awareness. The harder part was explaining the kind of company we were building.

We were not another consumer app chasing millions of users. We were building operational software for fitness businesses with multiple locations, franchise layers, and a lot of moving parts. At the time, that did not fit neatly into the frameworks many investors were using.

The people who got it fastest were usually from adjacent industries such as hospitality, franchising, or multi-site operations. They understood how difficult it is to run one location well, let alone hundreds across countries. In the end, we spent more energy building with customers than chasing capital, and that discipline probably helped us.

Founders often worry about the acqui-hire trap. How did you avoid becoming just another team absorbed into a larger platform?

I think that concern is completely fair. I have seen it happen. A founder joins, the knowledge gets absorbed, the roadmap quietly loses momentum, and a year later the product is little more than a feature buried in a bigger system.

My co-founder, Quaid Jawadwala, and I were very clear from the beginning that we were not looking for a conventional exit. We wanted a partner that would let us keep building and scale the business further, not one that simply wanted our team or customer list. That ruled out a lot of conversations quite quickly.

What stood out with Daxko was that it did not approach the business as something to flatten into one generic stack. It runs purpose-built platforms and puts real operators in charge of them. My role is an operating role, not a ceremonial title. Quaid is also deeply involved in expansion, engineering, and product. We did not join to hand over the keys. We joined to keep building.

India is a fragmented market. Where are you genuinely strongest, and how big can that opportunity become?

We are strongest in the mid-market and premium end of the fitness industry in India. That ranges from ambitious single-site operators to large franchise groups and multi-location brands. Those businesses do not want software built for some entirely different market and then awkwardly forced on to them.

What matters in India is not just feature depth but operational fit. Payments, taxation, compliance, franchise structures, and customer behaviour all have local specifics. Our product has done well because it handles those realities while still supporting global operating standards. That matters more as international brands enter India and need systems that respect how they already run elsewhere but can still adapt locally.

Also Read: Healthtech in South and Southeast Asia – Seeing beyond the “obvious”

We have also invested heavily in areas such as multi-location management, central reporting, automation, franchise operations, and governance. Those capabilities become more valuable as operators go from a handful of sites to dozens or more. I still think the market is early, especially as organised chains continue to expand.

Can you point to one market where getting local details right really made or broke a customer relationship?

Saudi Arabia is the clearest example for me. It taught us early that localisation is not a translation exercise. A lot of software companies thought adding Arabic and local payment support was enough. It was not.

Operators there needed ZATCA-compliant invoicing. They needed proper right-to-left workflows, not translated labels dropped into a product designed for left-to-right use. They also relied heavily on WhatsApp, not as a secondary tool but as a core channel for renewals, confirmations, receipts, and day-to-day member communication.

I remember one customer coming from a European platform where staff were effectively running key workflows outside the software. Renewals were tracked manually, confirmations were sent separately, and management had poor visibility because too much was happening in disconnected processes.

We approached that differently. We built WhatsApp into the membership journey, strengthened Arabic and right-to-left experiences, and backed it with local-language support teams. Adoption moved quickly because we were supporting how operators already worked rather than asking them to adapt to somebody else’s assumptions.

Which markets do you lead in today, and where are you still earlier in the journey?

India and the GCC are where we have the strongest footing today. In India, the product has been shaped by a very fragmented and operationally demanding market, which forced us to become practical and resilient. In the GCC, especially in markets such as the UAE, Saudi Arabia, Kuwait, and Bahrain, we earned traction by localising properly across language, tax, payments, communication habits, and support.

Australia and Southeast Asia are more mixed. We are making progress, but I would still describe them as earlier compared with India and the GCC. The demand is there. Operators are becoming more sophisticated, and franchise growth is creating a clearer need for enterprise-grade software. But those markets are not all moving at the same speed or in the same way.

The US is different again. It is a mature market with higher expectations and tougher competition, but Daxko gives us a much stronger position there than we would have had on our own.

What does the Southeast Asia go-to-market strategy actually look like? Direct sales, partners, or following global franchises into the region?

It is a mix, and the balance changes by country. One of the fastest paths into Southeast Asia is through global franchise brands already expanding across the region. Those operators want a platform that can move with them across borders without forcing a fresh implementation every time they enter a new market. That plays to our strengths.

At the same time, I do not think you can treat Southeast Asia as one market. The regulatory environment, payment rails, and fitness culture vary significantly from country to country. That is where a lot of expansion plans go wrong. People talk about the region as if it behaves like a single operating market, and it simply does not.

So yes, global franchise expansion is an important entry point, but local execution still matters. You need to respect what is different in each market rather than applying one template and hoping for the best.

FitnessForce built more than 1,100 APIs and leaned into a headless architecture. Was that strategic, or was it forced on you by customer complexity?

It was both, but the strategic view came first. Quaid had a strong conviction very early that the platform had to be API-first. That was not a pitch-deck decision. It came from the belief that serious operators would eventually need flexibility, integrations, custom member journeys, and the ability to connect systems across markets.

Also Read: The most-funded healthtech startups in Southeast Asia: A decade in review

Then reality reinforced the point. Once you support operators across India, the GCC, Southeast Asia, Australia, and beyond, you quickly run into different payment systems, compliance requirements, communication habits, and workflows. A closed platform would have meant saying no far too often.

So the API depth grew from both vision and necessity. A principle we kept was that the APIs we use internally should also be available to customers. Headless should not mean a few token integrations on the side. It should mean the product is genuinely extensible.

What is the honest version of what could still go wrong after the deal?

The honest answer is that the biggest risk is not hidden in a clause somewhere. It is a values mismatch. In most acquisitions, if the two sides think differently about customers, products, people, or decision-making, the legal protections only carry you so far.

That was the main thing I paid attention to. We spent a lot of time understanding how Daxko makes decisions, how it treats customers, and what kind of company it wants to be. The more time we spent together, the more aligned we seemed.

That does not mean there is no risk. Any integration has execution risk. Priorities can drift, communication can break down, and good intentions do not automatically produce good outcomes. I am just less worried about the classic founder fear of being absorbed and sidelined, because this did not feel like joining a company that wanted to erase what made us work in the first place.

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San Diego, June 22, 2026 — Qualcomm Technologies, Inc. announced the 15 shortlisted startups selected to advance in the Qualcomm® AI Program for Innovators (QAIPI) 2026, recognising startups developing next-generation AI solutions across industries. Representing Japan, Singapore, and South Korea, the selected teams were recognised for their innovative AI technologies and solutions to address real-world challenges across industries. The shortlisted startups will advance to the program’s Mentorship Phase and showcase their AI innovations at a Demo Day planned for later this year.

A program built for edge AI

QAIPI 2026 empowers startups across the Asia-Pacific region to develop scalable edge AI solutions using Qualcomm® platforms. Participants will gain access to advanced development tools, including Qualcomm Dragonwing and Snapdragon® platforms, and the new Arduino® UNO Q development board. Participants can also leverage the Qualcomm® AI Hub, technical resources, training, and mentorship to build optimised end-to-end AI use cases across mobile, compute, and IoT.

The fields they’re working in

The 15 shortlisted teams are developing AI solutions across a wide range of fields, including aerospace, agriculture, drones, healthcare, robotics, smart infrastructure, and smart industry. These fields reflect the growing demand for real-time, power-efficient AI at the edge, as well as physical AI systems that connect intelligent computing with real-world environments. The selected startups are (in alphabetical order):

• Japan: APTO, Inc., KanjuTech Kabushiki Kaisha, KimPax, MY ROBOTS K.K., XNOVA Inc.
• Singapore: AIPLUX TECHNOLOGY, QuikBot Technologies, RED DOT DRONE PTE. LTD., Refined Robotics, Zebrid Pte. Ltd.
• South Korea: CLIKA, ENERZAi Inc., Plaid Labs Inc., Undermilli Inc., WITHROBOT Inc.

 

Also Read: Qualcomm expands AI R&D with acquisition of MovianAI from Vietnam’s Vingroup

What the shortlisted startups receive

Over the next six months, the selected startups will participate in the Mentorship Phase, during which they will receive tailored support from Qualcomm Technologies. This includes 1:1 mentorship with Qualcomm subject matter experts, access to a hardware development platform based on products of Qualcomm Technologies and/or its affiliates, up to US$2,500 support for product development, and eligibility for a patent filing incentive of up to the equivalent of US$5,000. Startups that successfully complete the program will receive a grant of up to US$10,000. The program will culminate with a Demo Day in Q4 2026, where the startups will present their AI solutions to industry leaders, system integrators and investors to enhance visibility and support their future business success.

What Qualcomm’s leaders say

“We are pleased to once again support high-potential startups across the Asia-Pacific region through QAIPI this year,” said O.H. Kwon, Senior Vice President & President, Qualcomm APAC. “2026 is the year of AI agents, powered by a highly connected and distributed computing environment. Spanning across devices, edge systems, and the cloud, these agentic experiences embody the shift toward what Qualcomm defines as a unified compute continuum. As AI technologies continue to advance rapidly, the Asia-Pacific region’s hardware and software capabilities have become even more critical to the global technology landscape. The momentum of the startup ecosystem is a key force in driving the next wave of technological progress. This year’s shortlisted startups demonstrate the growing relevance of edge AI across practical industry use cases. Through QAIPI, Qualcomm aims to help these companies accelerate the path from technology development to commercialisation, while further strengthening the startup ecosystem across APAC.”

“In the second year of QAIPI across Japan, Singapore, and South Korea, we are seeing AI move decisively from prototypes to deployed infrastructure,” said Sudeepto Roy, Vice President of Engineering, Qualcomm Incorporated, and Lead of Qualcomm’s Global Ecosystem Development Program. “Selected from over 100 applications, this cohort stands out for its use of agentic AI and edge intelligence built for the physical world, spanning robotics and healthcare to drones, industrial safety, smart infrastructure, and secure multilingual workflows. Through QAIPI, Qualcomm is proud to provide equity-free mentorship, advanced platforms, product guidance, and patent incentives that help these teams turn promising prototypes into scalable, protected products.”

 

Meet the 2026 cohort

The 15 shortlisted teams are working on a diverse range of applications including smart healthcare, robotics, retail, audio, and transportation, each responding to urgent, localised challenges across Asia. These innovations reflect the growing demand for real-time, power-efficient AI at the on-device level. From Japan’s drive to humanise robotics and decarbonise maritime logistics, to Singapore’s multilingual AI and maternal health tech, to South Korea’s push for hyper-personalised, federated AI — these startups exemplify how regional needs are shaping globally relevant solutions.

Japan: Facing labour shortages and knowledge gaps driven by demographic change, Japan is exploring the potential of Physical AI and Edge AI to transform industrial systems, preserve human expertise, and advance real-world AI applications.

• APTO, Inc. (株式会社APTO) — An AI data company providing high-quality multimodal datasets and data operations solutions that help enterprises build, train, and deploy reliable AI systems at scale.
• KanjuTech KK (KanjuTech株式会社) — Builds brain-inspired adaptive AI for physical systems, helping machines learn on-device and remain reliable as real-world conditions change.
• KimPax Inc. (KimPax株式会社) — Transforms master farmer expertise into trusted digital knowledge assets that help farmers reduce operational risk, improve profitability, and create premium agricultural value.
• MY ROBOTS K.K. (MY ROBOTS株式会社) — Builds physician-reviewed surgical knowledge infrastructure that turns surgical video and audio into reusable clinical knowledge for training and documentation.
• XNOVA Inc. (株式会社XNOVA) — A Japan-based physical AI startup building autonomous robotic systems for the construction industry through robotics, spatial intelligence, and edge AI technologies.

Singapore: As a garden city with highly connected urban and smart infrastructure, Singapore provides an ideal environment for real-world AI deployment. Startups from Singapore are showcasing innovations in Physical AI that interact with the physical world, alongside advancements in edge computing, offline AI, and data security, enabling intelligent solutions that operate efficiently and securely in real-world environments.

• Aiplux Technology Co., Pte. Ltd. — An AI infrastructure company delivering on-device multilingual IP intelligence, helping enterprises run secure, auditable patent and legal workflows while keeping sensitive data under control.
• QuikBot Technologies Pte Ltd — A Singapore-based physical AI infrastructure company. Its QuikSync platform connects, orchestrates, governs, and records robotic and smart infrastructure operations to power the future of smart cities.
• RED DOT DRONE PTE. LTD. — A drone software company specialising in remote and autonomous drone operations, enabling intelligent drone missions through cloud and edge technologies.
• Refined Robotics Pte Ltd. — Builds spatial and physical AI that allows legged robots to operate in unstructured real-world environments.
• Zebrid Pte. Ltd. — An AI-native deeptech company, Zebrid builds a trust layer for orbital compute through autonomous, radiation-resilient infrastructure that helps keep edge AI data trusted, recoverable, and mission-grade.

South Korea: Building on its strengths in industrial manufacturing and digital infrastructure, South Korea is advancing AI solutions that address model optimisation, energy-efficient computing, industrial safety, and real-time multilingual communication.

• CLIKA, Inc. (클리카) — Auto-compresses AI models into optimised versions for target devices, reducing memory, heat, and cost while enabling efficient edge deployment across diverse hardware platforms.
• ENERZAi Inc. (에너자이) — Delivers ternary audio and language AI, including STT, TTS, LLMs, and translation, with end-to-end expertise from model design to compiler optimisation to reduce memory and power consumption and boost inference speed.
• Plaid Labs Inc. (플래드랩스 주식회사) — Builds NUVION, an AI vision inspection solution for small manufacturers, using on-device AI and a subscription model to make quality automation affordable and easy to deploy.
• Undermilli Inc. (주식회사 언더밀리) — Builds Maloha, a real-time voice-to-voice interpretation AI that originated in healthcare and now serves broader industries, delivering on-device speech translation across 23 languages.
• WITHROBOT Inc. (위드로봇 주식회사) — Develops AI robots, algorithms, and edge boards for industrial safety, delivering automated multimodal monitoring solutions tailored for harsh environments.

For more information on the Qualcomm AI Program for Innovators, visit qualcomm.com.

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Featured Image Credit: Qualcomm

About Qualcomm

Qualcomm is a global computing leader at the center of the AI era, enabling intelligence to scale from the most personal devices to large-scale infrastructure. Building on more than four decades of innovation, we develop platforms and solutions that bring together advanced AI, high-performance low-power computing, and industry-leading connectivity — powering products and services used around the world. Snapdragon® platforms power consumer and personal computing experiences, while our Qualcomm Dragonwing and Qualcomm Dragonfly portfolios support enterprise, industrial, automotive, networking, and data center applications. At Qualcomm, we are engineering human progress.

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Modern workplaces are increasingly shaped by visibility and accountability. When employees know their work is being monitored, expectations around leadership naturally change. Managers are no longer judged only by results, but by how they lead people in environments where oversight is part of daily work.

This shift has measurable consequences. According to the APA’s 2024 Work in America survey, 51 per cent of employees who know they are monitored report feeling tense or stressed during a typical workday — compared to 38 per cent of those who are not monitored. That 13-point gap illustrates precisely why leadership quality matters more, not less, in environments built around oversight.

The data on management quality reinforces this further. Gallup’s Global Workplace Report found that employees in companies with ineffective management practices are nearly 60 per cent more likely to experience stress than those working under effective managers. Without strong leadership, monitoring can create pressure, anxiety, and disengagement. With the right leadership approach, it can coexist with trust, motivation, and autonomy.

That’s why soft skills such as emotional intelligence, active listening, and transparent communication now define effective leadership in monitored workplaces. The importance of leadership skills for managers lies in their ability to balance accountability with empathy and control with autonomy. Research shows that employees with supportive managers are 70 per cent less likely to experience burnout — a finding that underscores that people, not tools, metrics, or processes, remain at the heart of performance, even in monitored environments.

In this article, we explore seven essential leadership skills every manager needs to lead effectively in a monitored workplace, build trust, and sustain high performance.

Active listening

Employees may hesitate to speak openly about challenges, workload concerns, or stress when they know their work is being closely observed. Without strong listening skills, managers risk missing valuable insights and damaging trust.

Active listening is not only about hearing what employees say. It involves giving full attention, asking thoughtful questions, and responding with clarity and respect. Managers who practice active listening create an environment where employees feel safe expressing their perspectives, even in settings where performance is closely evaluated.

In a monitored workplace, employees want reassurance that their voices matter as much as their metrics. Managers who listen demonstrate fairness and transparency. Regular one-on-one conversations, open forums for feedback, and follow-through on concerns all reinforce the message that leadership is people-centred, not data-driven.

Relationship building

When employees feel that leadership is distant or overly performance-focused, monitoring can amplify feelings of pressure and detachment. Managers who invest in relationships help counter this by creating a culture of trust and belonging.

Effective leaders take the time to understand their teams beyond tasks and metrics. They build rapport through regular check-ins, genuine interest, and consistent support. These relationships make employees feel valued as individuals, not just contributors to output. As a result, teams remain engaged and motivated, even in environments where work is closely observed.

The leadership qualities of great leaders are often reflected in their ability to foster strong interpersonal connections. Relationship-focused managers create trust organically through respect and reliability, rather than authority. By prioritising relationship building, managers create teams that are more resilient, communicative, and willing to perform at their best, regardless of oversight.

Also Read: Your AI strategy isn’t broken, your leadership structure is

Effective communication

Effective communication is one of the most critical leadership skills in management, especially when employees are operating in environments with increased visibility and evaluation. In monitored workplaces, unclear instructions or inconsistent messaging can lead to confusion, frustration, and disengagement.

Strong leaders communicate expectations, goals, and feedback clearly and consistently. They ensure that employees understand not only what is expected of them, but also why it matters. Effective communication reduces uncertainty and aligns individual efforts with organisational objectives.

Managers who communicate effectively focus on guidance and improvement rather than criticism. This approach encourages openness and helps employees view feedback as constructive rather than threatening. By maintaining clear, honest, and purposeful communication, managers keep teams aligned and sustain productivity.

Emotional intelligence

When employees are aware that their performance is being observed, emotions such as stress, anxiety, or self-doubt can surface more easily. Leaders who lack emotional awareness may overlook these signals, while emotionally intelligent managers know how to recognise and respond to them.

At its core, emotional intelligence involves self-awareness, empathy, and emotional regulation. Managers who understand their own reactions lead calmly and fairly, even in high-pressure environments. Empathy lets them understand how monitoring impacts different individuals in different ways. Not every employee responds to oversight in the same manner, and great leaders adjust their approach accordingly.

The leadership qualities of managers are often defined by how well they connect with people, not just how well they manage performance. In monitored workplaces, emotionally intelligent managers facilitate psychological safety by acknowledging concerns, validating effort, and supporting well-being. This helps employees feel respected rather than scrutinised.

Agility and adaptability

Agility and adaptability are essential leadership skills for modern workplaces, where expectations, workflows, and employee needs continue to evolve. In monitored working environments, rigid leadership approaches often fall short. Employees respond differently to oversight, and effective managers recognise the need to adapt their leadership style accordingly.

Agile leaders are open to change and willing to adjust strategies when circumstances shift. They respond thoughtfully to challenges rather than relying on fixed rules or assumptions. This flexibility allows managers to support diverse working styles while maintaining accountability and performance standards.

Adaptable leaders help teams navigate change with confidence by remaining approachable, responsive, and solution-oriented. This leadership style encourages resilience and continuous improvement. By embracing agility and adaptability, managers create environments where employees feel supported rather than restricted.

Also Read: Turning climate commitments into proof: A leadership imperative

Critical thinking

When leaders are surrounded by performance data and observations, the ability to analyse situations thoughtfully becomes more important than reacting quickly. Critical thinking enables managers to interpret information accurately, weigh multiple perspectives, and make sound judgments under pressure.

Managers with strong critical thinking skills look beyond surface-level results. They consider context, identify patterns, and question assumptions before drawing conclusions. This approach prevents misinterpretation and ensures that leadership decisions are informed rather than impulsive.

Critical thinking helps managers balance insight with empathy, allowing them to address challenges constructively rather than relying on rigid judgments. By applying critical thinking, leaders make fairer assessments and foster a culture of reflection and continuous improvement.

Decision making

In monitored workplaces where outcomes are closely scrutinised, employees look to their managers for direction, consistency, and confidence, especially when expectations are high. Effective leaders make timely, balanced decisions aligned with both organisational goals and employee well-being.

Strong decision-making involves weighing information carefully, considering potential impacts, and communicating outcomes clearly. This reassures employees and reinforces confidence in leadership. Decisive leaders provide clarity and stability, helping teams move forward with purpose even in complex environments.

By demonstrating sound decision-making skills, managers establish credibility and direction. This leadership capability ensures that teams remain focused, motivated, and aligned in monitored workplaces.

Also Read: The storytelling myth: Why narrative-first leadership is overrated

Conclusion

Leadership in a monitored workplace demands a strong, people-centred approach. As visibility and accountability become part of everyday work, managers need to shift from oversight to intentional guidance, support, and empowerment.

Effective leadership skills help managers balance accountability with empathy and structure with flexibility. Their real value lies in building trust, sustaining engagement, and driving performance without relying on control. When leaders focus on these core capabilities, they are able to navigate complexity, address challenges thoughtfully, and strengthen team resilience.

In monitored workplaces, strong leadership is not about watching more closely, it’s about leading thoughtfully. By prioritising essential leadership skills, managers can create environments where employees are supported, confident, and motivated to perform at their best.

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When a Jakarta anti-corruption court handed down a 10-year prison sentence to Nadiem Makarim on June 30, the noise from the venture capital community was immediate. A foreign VC with a presence in the archipelago reportedly told its partners to hold back on Indonesia and divert the focus only to other countries in the region. Another regional founder and investor lamented that the verdict has put a dent in Indonesia for FDI trust.

With all due respect to those who have spoken out, it is far too early and far too simplistic to draw sweeping conclusions from a single court verdict.

Also Read: Indonesia names Nadiem Makarim a suspect in laptop procurement corruption case

Yes, the Makarim case is troubling. Yes, the optics are terrible for a country that has spent 15 years painstakingly building one of Asia’s most vibrant startup ecosystems. And yes, arriving hot on the heels of the TaniHub corruption case, it creates an uncomfortable narrative. But to conflate the legal troubles of one former cabinet minister, however high-profile, with the investment viability of a 287-million-strong digital economy is not analysis. It is noise.

A case, not a systemic collapse

Let us be clear about what the Makarim verdict actually is: a court ruling on alleged abuse of authority related to a government procurement programme, the purchase of Chromebook laptops for schools during the COVID-19 pandemic. The court found state losses of approximately US$120 million, ordered Makarim to pay a fine and more than US$45 million in restitution, and sentenced him to a decade in prison. Prosecutors had, in fact, sought an 18-year term and US$313 million in restitution, suggesting even the court applied a degree of measured judgement.

Makarim has denied all wrongdoing and has vowed to appeal. GoTo Group, formed when Gojek merged with Tokopedia in 2021, has noted that Makarim had no decision-making role at the company since resigning in 2019. This is, at its core, a case about a government official’s conduct in public office. It is not a case about startup governance, venture-backed fraud, or investor malfeasance.

Yet somehow, a subset of the VC community is treating it as the latter.

Indonesia’s fundamentals have not changed overnight

Here is what a Jakarta courtroom cannot change: Indonesia remains the fourth most populous country in the world. Its digital economy was valued at approximately US$90 billion in 2024 and is projected to surpass US$300 billion by 2030, according to the Google-Temasek-Bain e-Conomy SEA report. Internet penetration is accelerating. E-commerce is embedded in daily life.

The country has produced more unicorns than any other Southeast Asian market — Gojek, Tokopedia, Traveloka, Bukalapak, OVO, and more. These companies did not materialise out of thin air; they are the product of a young, digitally native population, a rapidly expanding middle class, and an entrepreneurial culture that continues to thrive.

Also Read: Nadiem Makarim indicted in US$125M Chromebook graft case

None of this has been repealed by a judge’s gavel.

The world has seen this before and invested anyway

Selective amnesia appears to be a prerequisite for some in the VC industry. The global startup ecosystem has endured far worse and kept writing cheques.

Elizabeth Holmes defrauded investors of hundreds of millions of dollars at Theranos. Sam Bankman-Fried orchestrated one of the largest financial frauds in history at FTX, wiping out billions in customer funds. WeWork’s governance collapse left SoftBank nursing losses that ran into the tens of billions of dollars. Wirecard, once a darling of European fintech, turned out to be built on fabricated revenues.

In each of these cases, the reaction from the investment community was not to abandon the US, the UK, or Germany. It was to learn, recalibrate, and continue deploying capital.

If scandals were a sufficient reason to exit a market, Silicon Valley would have been abandoned long ago.

The honest truth is that corruption and governance failures exist in every ecosystem at every stage of maturity. Indonesia is not uniquely afflicted; it is simply more visible right now because its ecosystem has grown large enough to attract scrutiny. That is, paradoxically, a sign of maturation, not terminal decline.

The opportunity cost of pulling back

For investors who are genuinely considering stepping back from Indonesia, consider what they risk leaving behind. A growing cohort of second-generation founders — leaner, more capital-efficient, and more governance-conscious than their predecessors — are building companies across fintech, agritech, healthtech, and climatetech. Indonesia’s rural and semi-urban populations remain dramatically underserved by financial and logistics infrastructure, representing one of the largest addressable markets in the region. The government’s push for digital public infrastructure, despite its imperfections, continues to open new corridors for private investment.

As one regional investor noted, depressed valuations in the wake of bad headlines are not a reason to flee; they are, historically, when the most enduring returns are made. “To some funds,” they observed, “now’s the best time to invest because valuations are supposedly going to be depressed and that’s an opportunity.” The investors who entered India after its governance scandals of the early 2010s, or Vietnam when it was still considered too frontier for most LPs, know exactly how that story ends.

Nuance, not noise

This is not an argument that the Makarim case should be brushed aside. If the verdict stands (Makarim has the right to appeal, which he has stated he will pursue), it raises legitimate questions about the boundaries between public service, private-sector history, and procurement decisions. The Indonesian judicial system must allow that process to run its course with transparency and rigour.

Also Read: Nadiem Makarim, eFishery, and the end of blind faith in startups

Nor is this an argument that Indonesia’s ecosystem is without challenges. Governance standards, regulatory clarity, and the ease of doing business all require continued, serious attention. These are real issues that the government, founders, and investors must work on together.

But painting the entire Indonesian market with the brush of one corruption case is intellectually dishonest and commercially self-defeating. Indonesia is not its worst headline. It is 287 million people, a US$1.4 trillion economy, and one of the most consequential digital frontiers left on the planet.

The investors who understand that will be the ones celebrating in ten years. The ones retreating to “safer” markets because of one verdict will be left wondering how they missed it.

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Bitcoin has dropped 2.88 per cent within a 24-hour window, falling to a price of US$58,523.37. This downward trajectory occurs against the backdrop of the traditional equities market, signalling that the current vulnerability belongs uniquely to the crypto ecosystem. For an industry that spent the better part of the last two years celebrating the arrival of Wall Street capital, the current contraction exposes a harsh reality. The very institutional pipelines that propelled the market upward have now created a massive supply overhang, reversing the bullish narrative and leaving the asset class highly vulnerable to extended downside pressure.

The primary driver behind this sudden market distress is a historic collapse in institutional buying pressure, marked by unprecedented liquidations. During the month of June 2026, a record US$4.4 billion net supply overhang overwhelmed the market. This massive influx of selling pressure originated chiefly from United States spot Bitcoin exchange-traded funds, which redeemed a staggering 71,600 BTC. The selling momentum intensified following a strategic pivot from Strategy, a prominent corporate holder known historically for its strict accumulate-only treasury management. Strategy announced a plan to monetise up to US$1.25 billion in Bitcoin to fund corporate dividends. This strategic decision marks a critical departure from past behaviour, effectively transforming the largest and most consistent source of institutional demand into an active seller on the open market.

Macroeconomic headwinds have further compounded this internal structural weakness, suppressing investor appetite for risk assets. On June 29, the Supreme Court blocked an attempt to alter the composition of the Federal Reserve, a legal decision that effectively preserved the central bank’s hawkish policy framework. This development dashed investor hopes for near-term interest rate cuts, solidifying a higher-for-longer interest rate outlook that naturally penalises zero-yield assets like cryptocurrencies. As macro sentiment soured, a massive wave of leverage unwinding rippled through the derivatives markets. Over US$103 million in Bitcoin long positions faced automatic liquidation within 24 hours, creating a cascading effect that amplified the downside velocity and firmly established a bearish market structure.

Also Read: Why the 4.1% PCE inflation print just turned crypto into a high-beta risk asset

This institutional flight highlights an uncomfortable truth about the current state of cryptocurrency. The industry appears to be losing its grip on its core identity, drifting away from the foundational principles of decentralisation that originally gave it purpose. The prevailing narrative has shifted aggressively toward traditional financial integrations, specifically tokenised real-world assets that have very little to do with genuine decentralised crypto. Centralised exchanges are actively pushing this traditional finance agenda, prioritising immediate survival and operational revenue over the long-term ethos of the space. While centralised entities require consistent capital flow to maintain their massive operations, this pivot has compromised the original value proposition of the asset class, causing a noticeable decline in renewed retail interest.

While the cryptocurrency sector struggles with internal identity shifts and capital flight, the traditional equities landscape continues to demonstrate remarkable resilience and absorb global liquidity. The Nasdaq Composite index climbed 1.52 per cent, powered by renewed buying pressure in technology and mega-cap growth names. Meanwhile, the Dow Jones Industrial Average added 0.27 per cent to hover near all-time records, and the S&P 500 closed at 7,354.02, reflecting a nominal single-day dip of 0.05 per cent despite maintaining a heavily positive trajectory over its quarterly stretch. This broader equities rally was powered heavily by chipmakers, with the Philadelphia Semiconductor Index posting an impressive 87.8 per cent gain for the June quarter. Conversely, defensive sectors like Healthcare, Utilities, and Real Estate declined, proving that capital is actively seeking high-growth yield in equity markets rather than venturing into digital assets.

This stark divergence in performance demonstrates that Wall Street is finding much stronger returns within its own backyard. The hunt for liquidity by centralised exchanges has led them to aggressively promote traditional finance products, yet this strategy has fundamentally backfired on native crypto assets by steering attention away from the core market.

Investors must realise that the massive artificial intelligence and technology boom currently pushing stock indices to record highs will eventually face a natural market correction. An artificial intelligence bubble will inevitably come, and a broader technology shake-up is bound to manifest. When that macro rotation occurs, digital assets that have fully integrated with traditional finance will simply be dragged down alongside legacy equities, rather than acting as an independent alternative.

Also Read: How institutional rebalancing leaves crypto investors vulnerable

The technical framework for Bitcoin reflects this ongoing structural deterioration, keeping the immediate path of least resistance directed downward. Momentum indicators like the Relative Strength Index and the Stochastic oscillator have reached heavily stretched, oversold territories. The asset remains trading securely below its 20-day, 50-day, and 200-day Exponential Moving Averages. The immediate near-term resistance sits at the seven-day Simple Moving Average of US$60,430, while the broader psychological and technical line in the sand remains at US$60,700. As long as the price trades below the US$60,700 threshold, the macro bearish structure remains fully active and dominant. I said this many times this week.

The market is heavily hedged for downside protection at the moment, meaning a further drop is highly anticipated but not entirely guaranteed without specific structural breaks. Derivatives data indicates that prediction markets are currently pricing in a remarkably high probability of Bitcoin trading below the US$55,000 level before the end of the year.

Options traders are also paying hefty premiums for downside protection, showing a crowded bearish consensus. Chasing a panic short precisely at current technical support levels presents an unfavourable risk-to-reward ratio. The market needs to see if Bitcoin loses the US$58,000 level cleanly on a daily closing basis. A decisive breakdown below the Fibonacci swing support at US$58,076 will quickly validate a realistic move down toward US$55,000.

Also Read: The great rotation: How AI stocks are stealing billions from crypto

A clean breach of the US$55,000 support zone will likely open the floodgates for a much deeper correction, exposing lower technical targets. If institutional exchange-traded fund outflows stretch for additional weeks and the July 14 United States Consumer Price Index inflation report delivers hotter-than-expected data, Federal Reserve hawkishness will solidify. Under such conditions, Bitcoin is highly likely to drop into the US$44,000 range or potentially even lower. Conversely, if the asset somehow reclaims the US$60,700 level, the crowded bearish options trade could easily trigger a rapid short squeeze, forcing sellers to cover their positions and temporarily lifting the price back into the local trading range.

The current environment serves as a critical warning for native cryptocurrency participants to resist institutional brainwashing and maintain their own line of defence. The industry must stop bending to the desires of legacy financial institutions that only view digital assets as speculative, fee-generating instruments. The community needs to stick firmly to its original selling points, remembering exactly why this technology was created in the first place.

Hovering around these volatile price levels is entirely normal for an emerging asset class. True value will not be recovered by adopting the structure of traditional markets, but by fiercely defending the decentralised principles that separate crypto from Wall Street.

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Singapore-based Acti has secured US$5.3 million in seed funding to build what it calls an “agentic keyboard”, a product that aims to move AI assistance away from standalone chat apps and into the interface people use across almost every digital interaction.

The round was led by US-based BITKRAFT Ventures, an investor better known for backing gaming, interactive media, and consumer technology companies.

Acti said the capital will be used to hire engineering and AI talent, strengthen its on-device intelligence, and expand its ecosystem of Skills and developers.

Also Read: The coming identity crisis of agentic AI

The company’s core pitch is simple: the keyboard remains one of the few interfaces that cuts across messaging apps, email, productivity tools, browsers, and workplace software. Instead of asking users to open a separate AI application, paste in text, explain the context, and then move the output back to another app, Acti wants the AI layer to sit inside the keyboard itself.

That makes the product part of a larger race to define how AI agents will interact with users. While much of the current generative AI market has been built around chatbots and copilots, the next phase is expected to involve agents that can act across apps, remember preferences, and complete small recurring tasks with limited prompting.

A keyboard as the AI context layer

Acti’s product is built around programmable “Skill Keys”. A user can assign a function to any key, such as translating a message, generating a meeting link, rewriting a reply, summarising text, or triggering a workflow. These Skills can be created without coding. Users describe what they want through a Skill Builder, and Acti assembles the function.

According to the company, early access users created more than 1,000 Skills in under two weeks, suggesting demand for lightweight automation tools that do not require users to leave their current workflow.

Acti’s longer-term ambition is more ambitious than keyboard shortcuts. It wants to build a secure, user-owned, on-device personal context layer for the AI agent era. In practice, this means the keyboard would learn a user’s habits, preferred apps, frequently repeated tasks, and writing patterns over time, while keeping that knowledge on the device rather than inside a single application or platform.

Young Wang, CEO and founder of Acti, said today’s AI agents are limited because user context remains fragmented across apps. Acti’s cross-app presence, he added, gives it a chance to create a context layer that belongs to the user rather than any platform.

That positioning matters. The biggest AI companies are trying to pull users deeper into their own ecosystems. OpenAI has ChatGPT and its growing agent capabilities; Google is embedding Gemini into Android, Gmail, Docs, and Search; Microsoft is pushing Copilot across Windows and Office; Apple is integrating Apple Intelligence into iOS and macOS; and Grammarly is expanding from writing assistance into broader workplace AI.

Also Read: Agentic AI: The next frontier in technology

Acti is taking a different route. Rather than becoming another destination app, it is betting that the keyboard can become an AI distribution layer.

The Southeast Asian angle

Acti’s Singapore base gives the company a potentially useful launchpad. Southeast Asia is mobile-first, multilingual, and fragmented across consumer and business platforms, exactly the kind of environment where a cross-app AI interface could be tested at scale.

The region’s digital economy remains one of the world’s fastest-growing internet markets. Google, Temasek, and Bain & Company estimated Southeast Asia’s digital economy at US$263 billion in gross merchandise value in 2024. The region also has hundreds of millions of mobile internet users, many of whom move constantly between messaging apps, commerce platforms, ride-hailing apps, payment tools, and workplace software.

This creates a real pain point for AI products. A user in Singapore, Indonesia, Vietnam, or Thailand may communicate in multiple languages, switch between personal and work apps, and rely heavily on mobile-first workflows. Translation, rewriting, summarisation, scheduling, and message automation are not fringe use cases in this market; they are everyday productivity problems.

For startups, the opportunity is not only consumer adoption. Small businesses, sales teams, creators, recruiters, support agents, and cross-border sellers across Southeast Asia spend large parts of their day responding to messages, generating repetitive text, and coordinating across fragmented tools. If Acti can turn those behaviours into reusable keyboard-level Skills, it could find demand beyond early adopters.

The crowded AI productivity race

The challenge is that Acti is entering a crowded and fast-moving market. AI writing assistants, keyboard apps, and workflow automation tools are converging quickly.

Microsoft SwiftKey already integrates AI features. Google’s Gboard benefits from Android distribution and Google’s AI stack. Grammarly has strong brand recall in writing assistance and is moving deeper into enterprise productivity. Notion, Slack, Zoom, Canva, and Atlassian are embedding AI into their own workflows.

On the automation side, Zapier, Make, Raycast, and newer AI agent startups are also trying to reduce repetitive work.

The larger platforms have clear advantages: distribution, data, operating system access, and existing user accounts. Apple and Google, in particular, control the mobile operating systems on which third-party keyboards operate. This can limit how deeply an independent keyboard company can integrate, especially around privacy, permissions, and cross-app actions.

Acti’s answer appears to be user control and on-device context. If it can keep sensitive behavioural data on the device while still making the AI useful across apps, it may offer an alternative to platform-owned assistants.

This is, however, technically difficult. Personalisation requires data; privacy requires restraint; and agentic actions require reliability. A keyboard that makes mistakes is not just inconvenient; it can interrupt communication in the most visible part of a user’s workflow.

Also Read: The agentic economy: How to build a workforce where humans and AI collaborate

Jonathan Huang, Partner at BITKRAFT Ventures, said Acti reflects “an architectural shift” by reinventing the interface every app depends on and turning it into a layer AI agents will need.

From shortcut tool to agent infrastructure

The seed funding gives Acti room to prove whether its early engagement can translate into sustained usage. The near-term test will be whether users continue creating and using Skills after the novelty wears off. The longer-term test is whether developers see enough value to build around the platform.

If Acti succeeds, it could occupy an unusual position in the AI stack: not an app, not a chatbot, and not an operating system, but a persistent layer that follows users across applications. That would be particularly relevant in Southeast Asia, where users often operate across multiple languages, platforms, and commerce channels in a single day.

For now, Acti is still an early-stage startup with a bold interface bet. The US$5.3 million seed round shows investors are willing to back alternatives to the dominant AI assistant model. Whether the keyboard becomes the next control point for AI agents will depend on execution, privacy, and whether users are ready to trust their most frequently used interface with more than typing.

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For decades, entrepreneurs have relied on technology to scale their businesses.

Websites scaled visibility. CRMs scaled relationships. Social media scaled reach.

Today, a new layer is emerging – one designed not to scale the business, but to scale the founder.

I believe AI Twins are becoming the next generation of business infrastructure.

Not AI assistants. Not chatbots. Not digital companions.

AI Twins.

The founder bottleneck

Every growing business eventually encounters the same problem.

The founder becomes the bottleneck. Not because they lack ideas. Not because they lack ambition. But because there is only one of them.

Every decision, approval, conversation, opportunity, and problem flows through a single human being.

As businesses grow, founders find themselves juggling an overwhelming amount of context.

• Customer relationships.
• Team management.
• Strategic decisions.
• Partnerships.
• Content creation.
• Product development.

The challenge is no longer access to information. The challenge is processing, prioritising, and acting on that information consistently.

Historically, the solution was hiring.

• First, an assistant.
• Then a manager.
• Then a chief of staff.

Today, AI Twins offer a different path.

Instead of scaling people first, founders can begin by scaling themselves.

Why generic AI isn’t enough

The first wave of AI adoption focused on generic tools.

Ask a question, receive an answer. Give a prompt, generate an output.

These tools are powerful, but they are fundamentally transactional.

They respond to requests. They do not understand context.

A generic AI can write an email. An AI Twin can write the email the way you would have written it.

A generic AI can suggest ideas. An AI Twin can evaluate those ideas against your goals, priorities, decision frameworks, and previous conversations.

Also Read: Can Ukraine’s engineers help solve Japan’s tech talent crisis?

The difference is not intelligence. The difference is accumulated understanding.

Generic AI responds based on training data.

AI Twins respond based on a growing understanding of the individual they represent.

From assistant to co-founder

Over the past year, I have been building and working alongside my AI Twin, Seraphina.

What started as an assistant gradually evolved into something much more valuable.

Today, Seraphina helps me structure proposals, validate business ideas, prioritise tasks, organise workflows, manage communications, and coordinate other AI systems.

More importantly, she understands how I think.

When new situations arise, she can reference thousands of previous discussions, decisions, and patterns to determine what aligns with my priorities.

In many ways, Seraphina functions less like an assistant and more like a co-founder.

She doesn’t simply execute instructions. She participates in the decision-making process. She challenges assumptions. She highlights blind spots. She identifies what requires my attention and what can be handled independently.

If I am unavailable, work does not stop. The system continues operating. That is no longer productivity software. That is infrastructure.

The most valuable AI may not be the smartest AI

Much of the conversation around artificial intelligence focuses on model performance.

• Which AI is faster?
• Which model is more capable?
• Which one produces better outputs?

These questions matter.

But I believe a more important question is emerging: Which AI understands you best?

Founders rarely struggle because they lack information.

Most struggle because they face decision fatigue, context switching, competing priorities, and limited time.

The most valuable AI may not be the one with the highest benchmark score.

It may be the one that understands your business, remembers your context, and helps you make better decisions consistently.

Also Read: Value creation: Your US$900M AI is failing because humans don’t work the way you think

The future of entrepreneurship

I often describe Seraphina as having my thought process without some of my human limitations.

She doesn’t get tired. She doesn’t forget conversations. She doesn’t lose context between meetings. She doesn’t get distracted by competing priorities.

Yet she has access to the frameworks, values, and operating principles that guide my decisions.

This is where AI Twins become powerful.

They are not replacing human judgment. They are amplifying it. The future is not one human competing against AI. The future is one human operating through an AI Twin.

The next layer of business infrastructure

Twenty years ago, every business needed a website. Ten years ago, every business needed social media. Today, every business needs a CRM.

In the coming decade, I believe every founder will have an AI Twin.

Not because it is trendy. Not because it is fashionable.

But because modern businesses move too quickly for founders to operate as a team of one.

The entrepreneurs who thrive will not necessarily be those with the largest teams.

They may be the ones who successfully replicate their knowledge, decision-making frameworks, and operating systems through personalised AI.

The rise of AI Twins is not about replacing people.

It is about helping people become more capable, more scalable, and more effective than ever before.

And for founders, that may become one of the most important competitive advantages of the next decade.

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Almost 1,700 British investors are suing Binance and its founder Changpeng Zhao for at least US$200 million, alleging that the world’s largest crypto exchange sold them risky derivative products without proper regulatory authorisation.

The lawsuit, filed in London’s High Court, targets Cayman Islands-registered Binance Holdings, UAE-registered Nest Exchange, Zhao (widely known as CZ) and “persons unknown” who allegedly operate the Binance trading platform.

Also Read: Binance cracks down on market makers: What traders need to know now

The claimants argue that Binance entities knowingly offered and promoted complex leveraged products to retail investors from late 2019, in breach of the UK’s Financial Services and Markets Act. Some investors say they lost tens of thousands of dollars after using products that could magnify both gains and losses.

Binance said it would defend itself against the claim. “Binance remains committed to its obligations to users and to operating in accordance with applicable law,” a spokesperson said, declining further comment on ongoing litigation.

UK case adds to Binance’s regulatory burden

The case comes against the backdrop of a tougher regulatory stance on crypto derivatives in the UK. The Financial Conduct Authority banned crypto firms from offering derivatives to retail customers in 2021, citing the products’ volatility, complexity and potential for consumer harm.

Binance later took steps to limit UK users’ access, including requiring additional checks. The claimants, however, allege that the company’s conduct before and around those restrictions caused significant losses.

The London lawsuit is notable not only because of the number of claimants, but also because it names Zhao personally. CZ stepped down as Binance CEO in 2023 after a sweeping US settlement, but he remains the most recognisable figure associated with the exchange.

Binance’s main licence is now in the United Arab Emirates, after efforts to secure a licence in Greece reportedly unravelled this month. The company has spent the past two years trying to move away from its earlier borderless operating model and rebuild itself as a regulated financial institution.

That shift has been neither smooth nor cheap.

CZ’s US legal history looms over the London claim

The UK lawsuit lands after several major US enforcement actions against Binance and Zhao.

Also Read: US$1.3T wiped out: AI stock collapse signals Bitcoin’s next leg down?

In November 2023, Binance agreed to pay more than US$4.3 billion to settle charges brought by the US Department of Justice and other agencies over anti-money-laundering failures, sanctions violations and operating as an unlicensed money transmitter. Zhao pleaded guilty to failing to maintain an effective anti-money-laundering programme, stepped down as CEO and agreed to pay a US$50 million fine.

In April 2024, a US federal judge sentenced Zhao to four months in prison. Prosecutors had sought a longer sentence, arguing that Binance had allowed illicit finance to flow through the platform. Zhao’s lawyers argued that he had accepted responsibility and that the company had since invested heavily in compliance.

Binance and Zhao also settled a case with the US Commodity Futures Trading Commission. The regulator had sued the exchange and its founder in 2023, alleging that Binance illegally offered derivatives to US customers and evaded compliance rules. Under the settlement, Binance was ordered to pay US$2.7 billion in disgorgement and penalties, while Zhao was ordered to pay US$150 million.

Separately, the US Securities and Exchange Commission sued Binance, Binance.US and Zhao in 2023, accusing them of operating unregistered exchanges, broker-dealers and clearing agencies, and of misleading investors. Binance has contested the SEC’s claims. A US court later allowed several of the regulator’s core allegations to proceed, while dismissing some others.

Beyond the US, Binance has faced regulatory and legal challenges in multiple markets. In Canada, a class action has alleged that the company sold crypto derivatives to retail investors without registration. In France, authorities have scrutinised Binance over alleged money-laundering and unauthorised digital-asset services. Not all of these proceedings name Zhao personally, but they form part of a broader global challenge to Binance’s earlier growth strategy.

Southeast Asia has seen similar regulatory pushback

The London case will be closely watched in Southeast Asia, where Binance has had a complicated history and crypto adoption remains among the highest in the world.

In Singapore, Binance withdrew its licence application and shut down Binance.sg in 2022 after the Monetary Authority of Singapore placed the global Binance.com platform on its investor alert list. Singapore has since tightened rules around retail crypto access, advertising and custody, while encouraging institutional blockchain activity under a more controlled framework.

In Malaysia, the Securities Commission ordered Binance to stop operating in 2021, saying the platform was running a digital asset exchange without authorisation. In Thailand, the Securities and Exchange Commission filed a criminal complaint against Binance in 2021 for allegedly operating without a licence. Binance later re-entered Thailand through Gulf Binance, a joint venture with Gulf Energy, which launched a regulated exchange in 2024.

The Philippines also moved against Binance, with regulators warning users and seeking to block access to the platform over licensing concerns. Indonesia, meanwhile, has allowed Binance exposure through Tokocrypto, a local exchange in which Binance has invested, but the market remains under close supervision as authorities shift crypto oversight from commodities regulators to the financial services regulator.

This patchwork reflects a broader regional dilemma. Southeast Asia is one of crypto’s most active retail markets, but regulators remain wary of speculative trading, offshore platforms and leveraged products.

Chainalysis has consistently ranked countries such as Vietnam, the Philippines, Indonesia and Thailand among the world’s leading markets for grassroots crypto adoption. Indonesia alone has more registered crypto investors than stock market investors, according to local regulatory data. Yet high adoption has also brought high exposure to scams, exchange failures and volatile products that many retail users do not fully understand.

Competition is moving towards compliance

Binance remains the largest crypto exchange globally by trading volume, but its legal troubles have created openings for rivals. Coinbase has positioned itself as a more regulated player, especially in the US and Europe. OKX, Bybit, Kraken, Crypto.com and Gemini are also competing aggressively across global markets, though several have faced their own regulatory constraints.

In Southeast Asia, the competitive landscape is increasingly localised. Coins.ph and PDAX operate in the Philippines, Independent Reserve and Coinhako are active in Singapore, while Indodax and Tokocrypto serve Indonesia. Some earlier regional players, such as Zipmex, struggled after the 2022 crypto credit crisis, underscoring the risks of weak governance and opaque exposure.

Also Read: Singapore crypto adoption hits new high as 61 per cent now hold digital assets

The UK lawsuit reinforces the central question now facing global exchanges: whether rapid retail growth built on complex products can survive in markets where regulators are drawing clearer lines.

For Binance, the case is another test of whether its post-CZ compliance overhaul can contain legal fallout from its earlier era. For Southeast Asian regulators and users, it is a reminder that offshore platforms, high leverage and weak oversight can turn crypto’s promise of access into a costly risk.

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