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The Tokyo-based maker of NAND flash memory and solid-state drives has become one of the most closely watched stocks on the Tokyo Stock Exchange after a sharp rally tied to demand for data-centre storage, AI servers and high-performance memory systems. Its market capitalisation has climbed into the tens of trillions of yen, placing it near Toyota and behind SoftBank Group in a reshuffled ranking that would have seemed unlikely when Kioxia priced its initial public offering at 1,455 yen a share in December 2024.

The shift marks a wider challenge to the long-standing dominance of Japan’s manufacturing champions. Toyota, the global carmaker that symbolised the strength of Japan Inc. for decades, has been displaced from the top position by SoftBank during the latest AI-led surge, while Kioxia has risen rapidly from a former Toshiba memory unit backed by Bain Capital into a central player in the market’s technology rotation.

Kioxia’s climb reflects investor expectations that memory will remain a bottleneck in AI infrastructure. Generative AI systems require vast amounts of storage and high-speed data movement, pushing demand beyond the older cycle driven mainly by smartphones and personal computers. Data-centre operators, cloud companies and chip designers are competing for components needed to train and run large AI models, giving memory producers stronger pricing power after years of volatility.

The company’s earnings have strengthened sharply. Kioxia has projected operating profit of about 1.3 trillion yen for the April-June quarter, a figure that puts it among the most profitable listed companies in Japan. Its latest annual results showed strong momentum in revenue, margins and cash generation, helping ease earlier concerns about debt and cyclicality. Rating upgrades to investment grade have added to the perception that the balance sheet is improving at a time when investors are rewarding semiconductor exposure.

Kioxia’s origins give its rise broader industrial significance. The company traces its roots to Toshiba’s memory business, which was sold to a Bain-led consortium in 2018 after Toshiba’s financial crisis. The group was renamed Kioxia in 2019, drawing on the Japanese word for memory and the Greek word for value. Its public listing in 2024 valued it below 800 billion yen, after earlier IPO attempts were delayed by weak valuation conditions and pressure in the memory market.

That cautious debut contrasts sharply with its current status. Shares have multiplied as investors reassessed the role of NAND flash in AI systems, especially as large-scale data centres require higher storage density, faster access and more energy-efficient hardware. Kioxia has also been developing advanced BiCS FLASH technology and promoting next-generation SSDs for AI storage infrastructure.

The rally has also lifted questions over valuation risk. Memory chips remain one of the semiconductor industry’s most cyclical segments, with prices exposed to shifts in capital spending, inventory levels and customer demand. A sharp rise in market value leaves Kioxia vulnerable if hyperscaler investment slows, if competitors add capacity faster than expected, or if customers push back against higher pricing.

Competition is intense. Samsung Electronics, SK Hynix, Micron Technology and SanDisk remain key players across memory markets, with SK Hynix holding a dominant position in high-bandwidth memory used in AI accelerators. Kioxia’s strength is more closely tied to NAND flash and SSDs, where demand from AI infrastructure has broadened the market but does not eliminate the risk of oversupply once new capacity comes on stream.

Toyota’s relative decline in the rankings does not point to operational weakness alone. The carmaker remains one of the world’s largest manufacturers by sales and a major profit generator, but investors have been rotating away from traditional industrial leaders towards companies with direct AI exposure. Concerns over electric-vehicle competition, currency movements and the cost of future technologies have weighed on auto valuations, even as Toyota continues to command global scale in hybrid vehicles and supply-chain management.

SoftBank’s ascent has further highlighted the changing market narrative. Its exposure to Arm and AI-related investments has drawn investors seeking Japan-based access to global AI infrastructure. Together, SoftBank and Kioxia now represent a market story centred on chips, data centres, software platforms and computing capacity rather than the export-led industrial model that long defined Japan’s equity market.

Foreign investor flows have amplified the shift. Japan’s equity market has benefited from corporate-governance reforms, stronger capital returns and renewed interest in companies linked to the AI supply chain. The Nikkei 225 has reached record territory, though the rally has been uneven, with gains concentrated in a limited group of technology-linked names while broader market performance has been more mixed.

Kioxia’s next test will be whether it can convert favourable pricing and sold-out capacity into durable earnings. Investors will look for clearer shareholder-return plans, disciplined capital expenditure and evidence that AI storage demand can remain strong beyond the current investment wave. The company’s contemplated US share listing would widen access to global technology investors and could further raise its profile if market conditions remain supportive.

The shift reflects a sharp change in investor preference after months of underperformance by Hong Kong equities against mainland peers. Mainland investors sold about HK$3.6 billion of Hong Kong shares through the trading links last month, the first monthly net outflow since June 2023 and one of only a small number of monthly selling episodes since the southbound channel became a key route for mainland money into the city’s market.

The move is striking because southbound buying has been one of the strongest supports for Hong Kong stocks over the past year. Mainland funds poured into high-dividend banks, telecoms groups, energy companies and internet platforms during earlier phases of the rally, helping offset caution among global investors. That support is now weakening as domestic funds chase companies tied to AI infrastructure, chips, optical modules, software and computing power.

Onshore AI shares have delivered a stronger performance than Hong Kong’s technology benchmarks this year. The CSI Artificial Intelligence Index has risen sharply, while the Hang Seng Tech Index has lagged, weighed down by concerns over platform margins, heavy spending on AI models and pressure from subsidy battles in e-commerce and online services. The divergence has encouraged active money managers and retail traders to reassess whether Hong Kong-listed internet giants still offer the most attractive exposure to China’s technology story.

The change in flows also comes as Beijing’s industrial policy continues to favour advanced manufacturing, domestic semiconductors, large language models and high-end computing. Companies linked to AI servers, data centres, optical communications, robotics and chip design have become favoured market proxies for China’s effort to reduce dependence on foreign technology. That policy backdrop has helped support valuations even as questions persist over earnings visibility, capital expenditure demands and competition.

Cambricon Technologies, Zhongji Innolight, Kingsoft Office, iFlytek and other mainland-listed technology names have been among the companies watched closely by investors seeking domestic AI exposure. Hong Kong still hosts major technology groups such as Tencent, Alibaba, Baidu, Meituan and Xiaomi, but the market’s heavyweight structure makes it more exposed to mature platform businesses than to the faster-growing AI hardware and infrastructure supply chain favoured by onshore traders.

Hong Kong’s role as a fundraising centre remains intact despite the outflow. The city has continued to attract listings from technology, healthcare, robotics and semiconductor-related companies, while mainland firms still view it as the most practical offshore venue for raising international capital. Several AI and chip-linked companies are preparing or considering Hong Kong share sales, suggesting the city remains central to China’s capital market architecture even as secondary-market money rotates elsewhere.

The weakness in southbound appetite also reflects broader caution over Hong Kong valuations after a strong earlier advance. Investors who bought the market for dividends and policy support are locking in gains while seeking higher-beta opportunities at home. The shift does not necessarily point to a structural rejection of Hong Kong stocks, but it does show that mainland money is becoming more selective after helping drive earlier momentum.

Regulatory pressure on offshore investment channels may also be influencing behaviour. Authorities have tightened scrutiny of unauthorised cross-border trading accounts and offshore brokerage activity, reinforcing the appeal of regulated domestic routes. While Stock Connect remains an approved mechanism, wider efforts to keep capital within supervised channels have added another layer of sensitivity around outbound allocations.

Market analysts say the key question is whether the AI-led onshore rally can broaden beyond a narrow group of highly valued companies. China’s AI supply chain is benefiting from state support, falling model costs and growing enterprise adoption, but investors face risks from crowded positioning, uneven profitability and rapid technological shifts. Several AI-linked companies still trade more on expectations than on proven earnings, leaving the sector vulnerable to volatility if sentiment turns.

• FOMO Pay merchants in Singapore will soon be able to accept DuitNow QR payments, enabling Malaysian travellers and visitors to pay using familiar home-country banking and payment applications.
• The rollout comes ahead of the Johor Bahru–Singapore RTS Link, expected to carry up to 40,000 passengers daily upon launch in January 2027.

Enhancing Cross-Border Payment Experiences

Hashtag: #DigitalPayment #DigitalBanking #DigitalAsset #FinTech #AI

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https://www.linkedin.com/company/fomo-pay/
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About FOMO Pay

Visit www.fomopay.com for more information. For media inquiries, contact marketing@fomopay.com.

Advancing Research Commercialization and Building a Vibrant I&T Ecosystem

HONG KONG SAR – Media OutReach Newswire – 2 June 2026 – The Hong Kong University of Science and Technology (HKUST) today successfully hosted its annual “Unicorn Day,” one of Hong Kong’s flagship innovation and entrepreneurship events. The event brought together over 1,800 participants, including students, faculty, and alumni, as well as international and local government representatives, industry partners, investors, innovation and technology entrepreneurs, and researchers. Participants exchanged insights on strategies to strengthen collaboration across the innovation ecosystem, foster cross-disciplinary partnerships, and accelerate the translation of research outcomes into real-world impact.

The event was officiated by Prof. SUN Dong, Secretary for Innovation, Technology and Industry of the HKSAR Government, alongside consuls-generals and senior officials from countries including Australia, Chile, France, Hungary, India, Japan, Malaysia, the Netherlands, Vietnam, and the United Arab Emirates. They were joined by Prof. Nancy IP, HKUST President; Prof. Lionel NI, HKUST (Guangzhou) President; and Prof. Tim CHENG, HKUST Vice-President for Research and Development; in touring over 100 HKUST-nurtured startups, showcasing innovations and applications spanning areas such as healthcare, electronics, and artificial intelligence systems, as well as new materials, green energy, and sustainability.

Prof. Sun Dong remarked, “Innovation and technology (I&T) has always been at the top of the Hong Kong SAR Government’s agenda. We fully recognise the crucial role of local universities — not only as key partners, but as the bedrock of Hong Kong’s I&T ecosystem. In just three and a half decades, HKUST has risen to become a global powerhouse in deep tech, entrepreneurship, and innovation as demonstrated by its ability to nurture unicorns and deep-tech ventures. Hong Kong is entering a golden era of I&T development. We are building new engines to drive Hong Kong’s high-quality development, focusing on research and development in key fields and leading industry applications. I am confident that Hong Kong will rise as a leading international I&T center and contribute meaningfully to our nation’s high-quality development, and that HKUST will continue to be a driving force in this amazing journey.”

President Ip said, “As we mark the 35th anniversary of HKUST this year, we also celebrate the spirit of innovation and entrepreneurship that has long been embedded in our DNA. Both the Technology Transfer Center and the Entrepreneurship Center were established in the University’s early years to support faculty and students in realizing their entrepreneurial aspirations and to promote the translation of research outcomes into practical solutions. Over the past 35 years, the University has forged ahead with determination, sowing the seeds of innovation and yielding remarkable achievements across multiple frontier fields. Today, HKUST continues to broaden its horizons, strategically positioning itself in key domains. One example is medical education. The establishment of the HKUST Medical School will integrate the life sciences with cutting-edge technologies to nurture a new generation of interdisciplinary medical professionals.

Concurrently, HKUST has been actively involved in national initiatives such as the Chang’E 8 lunar exploration mission and research projects for the Tiangong Space Station, and the launch of the first commercially viable aerospace project under the RAISe+ Scheme. These achievements exemplify HKUST’s enduring ‘can do’ spirit over the past decades and echo the theme of today’s event—’From Ideas to Miracles—35 Years of Impact and Beyond’. Looking ahead, HKUST will continue to nurture bold ideas and action, working hand in hand with partners across different sectors to cultivate a vibrant innovation ecosystem, ensuring that research achievements benefit society and create lasting global impact.”

To support startups in expanding into global markets and to promote cross-border collaboration, HKUST introduced an “International Pavilion,” featuring startups and innovation organizations from various countries and regions. Participants from Thailand, Singapore and Korea were among those represented, strengthening connections between Hong Kong, and countries and regions along the Belt and Road in technology application and industry development.

The event also saw the signing of multiple partnership agreements, including six strategic collaborations with:

• GPTX Investment Management Limited
• China Everbright Water Limited
• Jiangsu Daotop Environmental Technology
• Samsung R&D Institute China – Guangzhou
• CalmCar Physical AI Innovation Center
• InnoAngel

to advance talent development and industry–academia integration, and foster innovation-driven research and real-world technology translation.

HKUST’s I&T Strength Driving Economic Growth

In addition, HKUST also released the report Driving Socioeconomic Impact Through Research and Innovation: Insights from HKUST, commissioned by the University and prepared by the international research information analytics organization Elsevier. The report highlights HKUST’s outstanding performance in innovation and entrepreneurship. Over the past five years, research output has recorded an average growth of 10%, approximately 9% higher than that of other local institutions. More than 65% of the research is concentrated in engineering and computer science, while 6.2% involves academia-industry collaboration, significantly higher than the global average of 2.7%. These collaborations have translated into impactful intellectual property, with approximately 1,670 patent applications and 700 grants over the same period, and a high IP utilization rate of above 30%.

Prof. Tim Cheng noted that the findings demonstrate HKUST’s steadily strengthening capabilities in research translation and academia-industry collaboration. HKUST’s publications achieved a Field-Weighted Citation Impact of 4.40, indicating that their citation impact is 4.4 times the global average. This underscores HKUST’s significant contributions to advancing Hong Kong’s socioeconomic development and enhancing its competitiveness in innovation and technology.

In addition, a “Marketplace” was also featured, showcasing products already launched by startups, including AI analytics tools, healthcare technologies, and smart city solutions, demonstrating the progress of translating research into practical applications.

Hashtag: #unicorn #unicornday #HKUST #partnership #innovation #startup #tech #AI #space #robotics #materialscience

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Financial markets are inherently complex systems shaped by price dynamics, capital flows, macroeconomic cycles, policy changes, and investor sentiment. Traditional financial modeling approaches, typically grounded in historical data, empirical assumptions, and linear analysis, face limitations in capturing such multidimensional interactions. AI for Science introduces new methodologies through simulation, advanced reasoning, and multivariable modeling, offering a broader analytical perspective.

The application of AI is not positioned as a replacement for traditional financial models, but as an enhancement to analytical capabilities in conditions of uncertainty. In areas such as asset valuation, risk exposure assessment, and macroeconomic scenario simulation, the central challenge lies in understanding variable interactions and maintaining model interpretability under extreme conditions. AI-driven approaches contribute to improved analysis by enabling more comprehensive scenario testing and deeper insight into systemic relationships.

Chow Kit Hui, founder of KitHui Growth Financial Academy, continues to monitor advancements in AI training architectures, algorithm optimization, and scientific research-grade artificial intelligence. Ongoing engagement with developments in intelligent decision-making and financial technology reflects a commitment to integrating emerging methodologies into financial research and education frameworks.

Insights from the Science x AI Summit 2026 indicate that the long-term value of AI extends beyond efficiency gains and content generation. Greater potential lies in supporting the analysis of complex systemic challenges. In financial applications, AI is not expected to eliminate risk or replace professional judgment; however, it can contribute to earlier risk identification, more robust assumption testing, and decision-making processes that more accurately reflect real-world complexity.

Hashtag: #ScienceXAI #ChowKitHui #AIforScience

https://www.kithuiacademy.com/

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About KitHui Growth Financial Academy

KitHui Growth Financial Academy focuses on advancing structured and long-term financial understanding across key areas including global asset allocation, corporate mergers and acquisitions, private equity, derivatives, risk management, macroeconomic research, and financial valuation modeling. The academy emphasizes systematic thinking and rational analysis rather than short-term market forecasting or speculative investment strategies.

The stock climbed about 6 per cent on Tuesday as investors reacted to signs that Tencent is moving closer to embedding a task-performing assistant inside WeChat, the messaging, payments and social platform used by more than 1.4 billion people. The prototype is designed to help users complete everyday actions within the app, including finding venues, interacting with mini-programmes and placing customised orders, rather than merely answering questions in chatbot form.

The planned agent would mark a significant step in Tencent’s push to convert its AI investments into consumer products at scale. WeChat already functions as a digital operating system for daily life in China, covering messaging, payments, shopping, food delivery, public services, entertainment, ride-hailing and enterprise communication. That reach gives Tencent an advantage that few rivals can match: a single platform with a vast user base, heavy daily engagement and a dense network of third-party services.

People familiar with the prototype have indicated that users would be able to access the AI agent through a swipe gesture on the main WeChat screen. Tencent is expected to begin the regulatory compliance process before wider testing, with a small group of external users likely to receive access before any phased public launch. A firm release date has not been set, reflecting both regulatory uncertainty and the technical demands of deploying agentic AI across a platform of WeChat’s size.

The market response underscores how investors are reassessing Tencent’s AI position after months of concern that the company was trailing Alibaba, ByteDance and Baidu in the race to commercialise generative AI. Tencent has promoted Yuanbao, its AI chatbot, developed its Hunyuan foundation model and introduced productivity-focused AI tools, but WeChat remains the asset most capable of turning those efforts into a daily consumer habit.

Tencent’s first-quarter earnings showed both the strength and the cost of that strategy. Revenue rose 9 per cent year on year to RMB196.5 billion, while IFRS net profit attributable to equity holders increased 21 per cent to RMB58.1 billion. Marketing services revenue rose 20 per cent to RMB38.2 billion, helped by AI-driven advertising improvements, while domestic games revenue grew 6 per cent and international games revenue climbed 13 per cent. Capital expenditure reached RMB31.9 billion, up 16 per cent, as spending on AI infrastructure continued to rise.

The company has told investors that new AI products are weighing on margins, even as core businesses continue to generate strong cash flow. Excluding new AI products, non-IFRS operating profit rose 17 per cent in the first quarter, compared with a 9 per cent increase overall. That gap highlights the near-term drag from AI investment, but also explains why investors are sensitive to any sign that Tencent can place AI directly into revenue-generating services.

Competition is intensifying across China’s technology sector. Alibaba has pushed Qwen into consumer and enterprise markets, ByteDance has expanded AI features across its content ecosystem, and Baidu continues to build around Ernie and AI cloud services. The key battleground is shifting from chatbots to agents that can perform tasks, connect with apps and act across digital services with limited user input.

WeChat’s mini-programme ecosystem could be central to Tencent’s approach. Millions of merchants, developers and service providers already operate within the platform, allowing an AI agent to navigate services without pushing users outside Tencent’s environment. For merchants, an effective agent could increase conversion rates by shortening the path from search to purchase. For Tencent, it could deepen engagement, improve advertising relevance and strengthen its fintech and business services ecosystem.

Regulation remains a critical constraint. China requires generative AI services offered to the public to comply with rules covering data security, content control and algorithmic governance. Any WeChat agent would need to satisfy those requirements before broad release, especially because it could interact with payments, commerce and public services.

The Provisions on the Protection of Trade Secrets, issued by the State Administration for Market Regulation under Order No. 126, came into force on 1 June 2026 and replace rules first introduced in 1995. The overhaul gives regulators clearer authority to handle disputes involving digital assets that now sit at the centre of corporate value, from AI model architecture and training datasets to platform recommendation systems, software code and commercially valuable operational data.

The change marks a significant shift in China’s treatment of intangible technology assets. Earlier rules were drafted before cloud computing, AI systems and large-scale data operations became central to business competition. The new framework defines trade secrets as technical or business information that is not publicly known, has commercial value and has been subject to confidentiality measures by the rights holder. Technical information now expressly covers data, algorithms, computer programs and code, while business information can include management, sales, finance, planning, customer and operational data.

The timing gives the regulation wider strategic weight. China and the United States are locked in a prolonged technology rivalry involving semiconductor controls, restrictions on advanced computing hardware, limits on investment in sensitive sectors and scrutiny of cross-border technology transfers. Washington has tightened export controls on high-end chips and chipmaking equipment, while Beijing has expanded its own legal toolkit to protect critical technology, secure data flows and prevent overseas transfer of capabilities it regards as strategically important.

The new rules do not create a separate trade secret law, but provide more detailed administrative guidance under China’s Anti-Unfair Competition Law. They clarify how regulators should assess whether information is secret, whether it has commercial value, what counts as reasonable confidentiality measures and how infringement should be investigated. For companies, the central message is that protection will depend not only on the value of the information, but also on whether internal controls, access limits, confidentiality agreements, digital safeguards and evidence trails are in place.

The provisions also recognise that unsuccessful research can have commercial value. Stage-by-stage technical results, failed experimental data and development paths that save time or cost in future research may qualify for protection if they meet the legal conditions. That is especially relevant for AI labs, pharmaceutical developers, electric vehicle makers, chip design firms and advanced manufacturing groups, where failed trials can reveal important technical boundaries and prevent competitors from repeating expensive mistakes.

Regulators have been given practical enforcement tools, including powers to inspect business premises, question individuals, copy documents, examine electronic records and take action against materials linked to suspected infringement. Penalties can include orders to stop unlawful conduct, confiscation of illegal gains and fines that may reach CNY5 million in serious cases. The framework also covers improper acquisition, disclosure, use or permission to use trade secrets obtained through theft, bribery, fraud, coercion, electronic intrusion or breach of confidentiality obligations.

For multinational companies operating in China, the new rules create a mixed picture. Stronger protection could help firms defend proprietary data, software systems and industrial processes in a market where trade secret disputes have often been difficult to pursue. At the same time, wider protection for data and algorithms may increase compliance complexity, particularly in joint ventures, outsourced research, cloud services, employee mobility, cross-border collaboration and licensing arrangements.

Technology companies are likely to face tougher expectations over documentation. Firms seeking protection will need to show that the information was not easily accessible, carried economic value and was actively protected. Generic claims that an algorithm or dataset is confidential may not be enough. Companies will be expected to classify sensitive assets, restrict access by role, track downloads and transfers, maintain logs, use encryption or watermarking where appropriate, and ensure that employment and partnership contracts define confidentiality obligations with precision.

The rules also sit alongside China’s broader data and security regime, including laws on data security, personal information protection, cybersecurity and export control. That overlap could make enforcement more powerful but also more complicated. A dataset may be a trade secret, a regulated data asset and a cross-border transfer concern at the same time. Businesses handling AI training material, user behaviour data or industrial performance data will need to assess those obligations together rather than treating trade secret protection as a standalone issue.