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TECO Electric & Machinery is expanding further into Southeast Asia’s AI data center infrastructure market through a new acquisition in Malaysia.

The Taiwan-based company has signed an agreement to acquire approximately 78 percent of Malaysian engineering firm Dynaciate Engineering in a deal valued at around MYR 200 million (US$50.8 million). According to TECO, the acquisition is aimed at strengthening its modular data center manufacturing capabilities and supporting its expansion across Southeast Asia’s data center infrastructure sector.

Under the agreement, Dynaciate will become TECO’s global manufacturing hub for modular data center and power equipment products. The company will also serve as an engineering hub supporting TECO’s regional expansion efforts, particularly in AI data center infrastructure projects.

TECO Chairman Morris Li said the integration between both companies has improved execution efficiency and increased the company’s in-house modular prefabrication capabilities. According to the company, the collaboration has reduced data center delivery timelines to as little as six months.

Dynaciate is headquartered in Johor Bahru, Malaysia. Its facilities span approximately 36,000 square meters and include eight production buildings focused on stainless steel and carbon steel fabrication. The company said the site is also eligible for export tax incentives that support future global supply chain deployment.

According to TECO, Dynaciate has experience in engineering, steel fabrication and large-scale industrial projects for multinational corporations. The company added that Dynaciate has expanded into the data center engineering market since 2025 through projects involving international cloud service provider clients.

TECO estimates that after the acquisition, around 65 percent of future data center-related revenue will come from modular data centers and prefabricated products, while the remaining 35 percent will come from AI data center engineering projects. The company also forecasts that data center-related revenue within its Power & Energy Business Group will rise from below 10 percent to 30 percent this year.

Dynaciate CEO Ng Kim Thiea said the company is entering a new phase of growth through the partnership with TECO. He added that Dynaciate has extensive experience supporting engineering and industrial projects across the region.

The acquisition marks a further expansion of TECO’s presence in the AI data center infrastructure sector as companies continue increasing investments in modular infrastructure and regional engineering capacity.

As AI companies continue scaling larger models and data centers, the pressure is no longer falling only on chip design. Manufacturing capacity, advanced packaging and infrastructure deployment are becoming equally important parts of the AI race. AMD’s latest investment announcement reflects how quickly that shift is accelerating.

The US chipmaker announced plans to invest more than US$10 billion across Taiwan’s semiconductor and manufacturing ecosystem to support next-generation AI infrastructure. The investment focuses on expanding partnerships and increasing advanced packaging capacity needed for future AI systems.

The announcement highlights a growing reality across the AI industry. Building powerful AI chips is no longer enough on its own. Companies now also need the manufacturing networks, packaging technologies and supply chain coordination required to deploy AI infrastructure at global scale.

AMD’s investments center heavily around advanced chip packaging, an area becoming increasingly critical as AI systems demand higher performance and greater power efficiency. Traditional chip architectures are struggling to keep pace with the size and complexity of modern AI workloads. Advanced packaging helps connect processors, memory and computing systems more efficiently while managing power and cooling limitations inside large-scale AI environments.

The company said it is working with Taiwan-based partners including ASE, SPIL and PTI to develop next-generation packaging technologies for its upcoming 6th Gen AMD EPYC processors, codenamed “Venice.” AMD also said it had qualified what it described as the industry’s first 2.5D panel-based EFB interconnect technology alongside PTI.

At the center of the broader strategy is AMD Helios, the company’s rack-scale AI infrastructure platform scheduled for deployment beginning in the second half of 2026. The platform combines AMD Instinct MI450X GPUs, 6th Gen EPYC CPUs, networking systems and AMD’s ROCm software stack into integrated AI infrastructure systems designed for hyperscale deployment.

Rather than selling individual processors alone, companies are increasingly building complete AI infrastructure platforms that combine hardware, software, cooling systems and power management into unified deployments. That transition is reshaping how AI infrastructure is designed, manufactured and delivered.

Taiwan is also becoming more deeply embedded in that process. AMD’s investment spans not only semiconductor packaging companies but also manufacturing and system integration partners including Sanmina, Wiwynn, Wistron and Inventec. The partnerships reflect Taiwan’s growing role as one of the operational centers of the global AI infrastructure economy.

Dr. Lisa Su, Chair and CEO of AMD, said: “As AI adoption accelerates, our global customers are rapidly scaling AI infrastructure to meet growing compute demand. By combining AMD leadership in high-performance computing with the Taiwan ecosystem and our strategic global partners, we are enabling integrated, rack-scale AI infrastructure that helps customers accelerate deployment of next-generation AI systems”.

Power efficiency is becoming another major challenge shaping AI infrastructure decisions. As AI workloads consume more electricity and generate more heat, infrastructure providers are increasingly being forced to rethink cooling systems, interconnect technologies and deployment economics.

AMD’s announcement signals how the AI competition is evolving beyond model development and raw computing power. The next stage may depend just as heavily on who can manufacture, package and deploy AI infrastructure fast enough to support global demand.

As drone adoption grows across industrial sectors, more startups are moving beyond hardware sales and into service-based business models. Instead of simply selling drones, companies are increasingly trying to build recurring revenue through inspection, mapping and infrastructure-monitoring services. That shift is shaping ZenaTech’s latest expansion strategy.

ZenaTech is a Vancouver-based startup that develops AI drone and Drone as a Service (DaaS) technologies. The company has signed an offer to acquire an Alberta-based land surveying and geomatics business operating across Western Canada. If completed, the deal would mark ZenaTech’s first land surveying acquisition in Canada and its first major push into the oil and gas sector.

The move gives the startup something more valuable than just another acquisition target. It provides direct access to an industry where drones are already becoming part of everyday operations.

The Alberta surveying company works with oil and gas producers across Alberta, Eastern British Columbia and Saskatchewan. Its services include land surveying, geomatics, mapping and environmental support for infrastructure and energy development projects.

According to ZenaTech, drones are already used in roughly 80 percent of the target company’s existing projects. That matters because it reduces the operational gap between traditional surveying work and AI-powered automation.

Rather than introducing drones into a completely manual workflow, ZenaTech is entering a business where drone-based data collection is already established. The startup says it plans to build on that foundation by integrating more AI-powered capabilities across surveying, mapping, inspections and infrastructure monitoring.

Shaun Passley, Ph.D., CEO of ZenaTech, said: "This proposed acquisition represents an important strategic expansion of our Drone as a Service business into Canada’s oil and gas sector, one of the most significant energy markets in North America. This company brings an established commercial customer base, strong regional expertise, and extensive experience supporting surveying and geomatics projects including for some large producers. We believe there is a significant opportunity to further enhance these services through AI-powered drone technology for surveying, mapping, inspections, and infrastructure monitoring applications, enabling us to establish a core expertise that we can bring to this fast-growing global industry."

The timing is also significant. ZenaTech pointed to estimates showing the global oil and gas drone inspection services market is currently valued at around US$ 2.3 billion and projected to grow at a compound annual growth rate of roughly 28.5 percent.

Much of that growth is being driven by energy companies looking for faster ways to inspect infrastructure, monitor remote sites and reduce manual field operations.

ZenaTech’s broader strategy centers around building a global DaaS network through acquisitions. Instead of creating local operations from scratch, the startup is acquiring existing service businesses with established customers and then layering drone automation and AI systems into those operations.

The company says its DaaS platform offers businesses and government clients subscription-based or on-demand drone services across areas such as inspections, surveying, maintenance, inventory management and precision agriculture.

The larger opportunity for startups in this space may not be drone manufacturing alone. Increasingly, the focus is shifting toward startups that can build scalable drone service networks and integrate them into industries that already rely on large-scale field operations. Oil and gas appear to be one of the next major targets for that expansion.

As AI companies build larger models and deploy more AI agents, the industry is running into a new constraint: electricity. The challenge is no longer just about computing power. It is increasingly about how to supply, manage and sustain the energy needed to run AI infrastructure at scale.

That was the central argument behind Huawei’s latest AI data center strategy unveiled at its Global AIDC Industry Summit in Dongguan.

The company introduced what it calls a grid-interactive AIDC strategy, focused on redesigning AI data centers around power supply, cooling systems and energy management. AIDC refers to AI data centers built specifically for large-scale AI computing workloads.

The announcement reflects a broader shift happening across the industry. As AI systems grow larger, data centers are consuming more electricity and generating more heat than traditional computing infrastructure was designed to handle. Companies are now being forced to rethink not just chips and servers, but the physical systems supporting them.

Huawei argues that future AI infrastructure will need closer coordination between computing systems and energy grids. The company says traditional data center designs are struggling to keep up with fluctuating AI workloads, rising power density and the growing use of renewable energy sources.

Hou Jinlong, Director of the Board of Huawei and President of Huawei Digital Power, said: "The booming AI industry, widely adopted large models, and numerous AI agents are creating huge energy demands, set to boost the global AIDC capacity. Electricity is essential for computing; energy is the foundation for AI long-term development. Computing and electricity will deeply synergize and empower each other, progressively building an integrated framework that brings together new power systems and AI infrastructure."

A large part of Huawei’s strategy focuses on power architecture. AI workloads can create sudden spikes in electricity demand, especially in high-density computing environments. To manage that, Huawei says it plans to develop new power systems that combine grid-friendly UPS infrastructure with energy storage technologies.

Cooling is becoming another major pressure point. AI servers generate significantly more heat than traditional enterprise systems and Huawei says liquid cooling is now becoming essential for large-scale AI deployments. The company introduced a liquid cooling system designed to improve long-term thermal management inside high-density AI environments.

Huawei is also pushing modular construction methods to reduce deployment times for AI data centers. Instead of building infrastructure entirely onsite, parts of the system can be prefabricated and tested in factories before installation.

Bob He, Vice President of Huawei Digital Power, said: "The global AI industry is booming, and the token demand surges. As such, the AIDC industry is entering the Token era."

As part of that shift, Huawei introduced a proposed measurement system called the TokEnergy Index. The company says the metric is designed to measure the relationship between energy consumption and AI computing output, rather than relying only on traditional data center efficiency metrics such as PUE.

The broader message behind the strategy is that AI infrastructure is becoming an energy engineering problem as much as a computing problem. As global demand for AI continues to rise, companies across the sector are beginning to realise that the future of AI may depend not only on better models, but also on whether power grids and data centers can keep up with them.

Investor interest in humanoid robotics is continuing to grow as startups race to build systems capable of working alongside humans in real-world environments. That momentum was reflected after WIRobotics announced a KRW 95 billion (USD 68 million) Series B funding round to accelerate development of its humanoid robotics platform, ALLEX.

The Seoul-based startup said the funding comes roughly two years after its KRW 13 billion Series A round in 2024. JB Investment led the financing alongside investors including InterVest, Hana Ventures, Smilegate Investment, SBVA, NH Investment & Securities, Company K Partners, GU Investment and FuturePlay.

WIRobotics has spent the past several years building wearable robotics systems designed to assist human movement. The startup is now using that foundation to expand deeper into humanoid robotics and Physical AI, a category focused on AI systems that can interact with the physical world through movement, perception and manipulation.

Its humanoid platform, ALLEX, is being developed to support human-level object manipulation and interaction capabilities. The startup was recently selected for NVIDIA’s Physical AI Fellowship, a global robotics and AI development initiative aimed at supporting next-generation robotics research.

Rather than building humanoid systems entirely from scratch, WIRobotics is drawing on movement data collected through its wearable walking-assist robot, WIM. Over the past three years, the startup says it has built large real-world datasets around gait patterns, mobility and human movement control.

That wearable robotics business has also started showing commercial traction. WIM has sold more than 3,000 cumulative units and expanded into overseas markets including Europe, China, Türkiye and Japan. Revenue grew from KRW 560 million in 2023 to KRW 1.3 billion in 2024, then to KRW 2.79 billion in 2025. According to the startup, first-quarter 2026 revenue has already surpassed its full-year 2024 total.

The startup believes that real-world movement data collected through wearable robotics could become a competitive advantage as humanoid systems move closer to commercial deployment. WIRobotics is also expanding its global footprint alongside its robotics development efforts. The startup said it is establishing a North American entity in California while growing partnerships with overseas distributors and healthcare networks.

Its humanoid ambitions are moving into a more operational phase as well. Beginning later this year, WIRobotics plans to supply a research-focused version of its Mobile ALLEX platform to global research institutions and international partners for testing and collaborative development. The startup is also in discussions with a global automotive manufacturer around manufacturing-focused platform validation projects.

Yeonbaek Lee said: "This investment represents global recognition that the real-world movement data and control technologies accumulated through wearable robotics can evolve into next-generation humanoid robotics. We aim to accelerate the arrival of humanoid robots capable of interacting naturally with people".

Yongjae Kim added: "All investors from our previous Series A round participated again in this Series B financing, demonstrating strong confidence in WIRobotics' technological capabilities and growth potential amid intensifying global humanoid competition. Our mission is to realize humanoids capable of fundamentally human-like interaction and force control, driving a paradigm shift in high-performance manipulation technologies".

As competition intensifies across humanoid robotics, startups are increasingly trying to differentiate themselves through real-world deployment data rather than simulation alone. WIRobotics is positioning its wearable robotics business as the foundation for that transition, betting that years of human movement data could help shape the next generation of humanoid systems.

Investors are beginning to place bigger bets on AI startups focused on drug delivery infrastructure rather than drug discovery alone. That shift was on display this week after METiS TechBio, a Hong Kong tech-bio startup focused on AI-powered drug delivery systems, debuted on the Hong Kong Stock Exchange.

The listing made METiS TechBio the world’s first publicly traded AI-powered drug delivery startup and the first AI-powered large-molecule biopharmaceutical startup listed in Hong Kong. The startup raised more than HKD 2.1 billion through its IPO, making it the largest healthcare listing in Hong Kong so far in 2026.

Investor demand was unusually strong. The Hong Kong public offering was oversubscribed by more than 6,900 times while the international tranche recorded 82 times oversubscription. More than 280 institutional investors participated in the international placing.

The strong demand reflects a wider shift in AI biotech. Over the past few years, much of the sector’s attention has focused on using AI to discover new drugs or molecules. METiS is taking a different approach. The startup focuses on how medicines are delivered inside the body after they are developed.

That challenge is becoming harder to ignore in biotech. Designing a therapy is only one part of the process. Delivering it precisely to specific organs, tissues or cells remains a major hurdle, especially for newer therapies involving RNA, proteins and large-molecule drugs.

METiS is trying to solve that problem through its proprietary NanoForge platform. The system uses AI to design and test nanodelivery systems that help medicines reach targeted areas inside the body more efficiently. The platform combines AI models, simulation systems and high-throughput screening tools to speed up formulation development and improve delivery precision.

The startup says it has already achieved targeted delivery across eight organs and tissue systems including the liver, lungs, heart, muscles and central nervous system.

One of its lead programs, MTS-004, became China’s first AI-enabled formulation drug to complete a Phase III clinical trial. The drug is being developed for pseudobulbar affect, a neurological condition that affects emotional expression. According to the startup, AI tools helped reduce preclinical formulation development time from up to two years to less than three months.

Investor interest in the IPO also came from some of the world’s largest asset managers and healthcare funds. BlackRock led the cornerstone investments with a USD 50 million subscription. Other participating investors included UBS Asset Management Singapore, Mirae Asset, ORIX Corporation, Deerfield, RTW, Hillhouse Capital and IDG Capital.

METiS is also building what it describes as a “platform collaboration + product partnership” business model. The startup currently works with more than 30 pharmaceutical and biotechnology partners globally, including large pharmaceutical companies and medical research institutions.

The company reported RMB 105 million in revenue in 2025, largely tied to upfront payments connected to its MTS-004 partnership agreements. It also said some platform collaboration contracts could reach milestone values of up to USD 109 million.

Chris Lai said: "The future of biomedicine will no longer be simply about 'taking medicine when one falls ill.' METiS TechBio's ambition is to harness AI to build nano-rockets that can navigate with precision through the inner space of the human body's 30 trillion cells, write the code of nucleic acids and proteins into cells, and reprogram diseased and aging cells into healthy cells. This was our founding aspiration, and it is the mission to which we will dedicate our lives. The IPO marks a new starting point for us to accelerate forward, and we will strive to live up to the support and trust we have received from all sectors."

The IPO also highlights how Hong Kong is increasingly positioning itself as a hub for next-generation biotech and AI healthcare startups. While investor excitement around AI drug discovery has cooled in parts of the market, startups focused on delivery systems and biotech infrastructure are beginning to attract stronger institutional backing.

For METiS, the challenge now will be turning that investor confidence into commercially viable therapies and long-term partnerships. But the listing suggests that AI-driven drug delivery is starting to emerge as a category investors are willing to treat as core biotech infrastructure rather than a niche research experiment.

As copyright disputes continue to grow around AI-generated music, Sonilo, the world’s first professionally licensed video-to-music AI platform, has partnered with Shutterstock to train its models on licensed music catalogs.

The agreement gives Sonilo access to Shutterstock’s music library for AI model training. According to the companies, it is Shutterstock’s first partnership with a video-to-music AI platform and the timing is significant. AI music companies are facing growing pressure over how their systems are trained. Artists and record labels have increasingly challenged the use of copyrighted music in AI datasets, especially when licensing agreements or compensation structures are unclear.

That tension has created a divide across the industry. Some companies have continued building models around scraped or disputed data. Others are trying to position licensing as part of the product itself.

Sonilo falls into the second group. The company says its models are trained only on licensed material where artists and rights holders have agreed to participate and receive compensation. The Shutterstock partnership strengthens that position while giving Sonilo access to a larger pool of commercially cleared music.

The collaboration also points to a broader change happening inside generative AI. As AI tools move into commercial production, companies are being pushed to show not just what their models can generate, but also where their training data comes from.

Sonilo’s platform is built around video rather than text prompts. The system analyses footage directly, studies pacing and emotional tone, then generates an original soundtrack to match the content. The company says this removes the need for manual music searches, syncing or editing workflows. The generated tracks are cleared for commercial use across social media, branded content and broadcast production.

Shawn Song, CEO of Sonilo, said: "Music has always been the last unsolved layer of video creation, and video has always carried its own soundtrack. We built Sonilo to hear it and compose from it, without a single text prompt. But how we build matters as much as what we build. While others have chosen to take artists' work without permission and charge creators for the privilege, we've chosen a different path—one where artists are compensated from day one. Partnering with Shutterstock reflects that standard. Every model we train meets a bar the music industry can stand behind, because the most innovative AI platforms don't have to come at the expense of the artists who make all of these possible."

For Shutterstock, the deal expands the company’s growing role in generative AI infrastructure. The company has increasingly focused on licensing content for AI systems across images, video and music.

Jessica April, Vice President of Data Licensing & AI Services at Shutterstock, said: "AI innovation depends on access to high-quality, rights-cleared content and trusted licensing partnerships. Sonilo's approach reflects the growing demand for responsibly sourced training data and commercially safe AI workflows. We're pleased to support companies building generative AI products with licensed content and scalable data solutions that help accelerate innovation while respecting creators and rights holders."

The partnership also comes as Sonilo expands into creator and developer ecosystems. Earlier this month, the company launched as a native node inside ComfyUI, an open-source AI workflow platform used by millions of creators. Sonilo also offers API access for integration into creator tools, video platforms, game engines and other AI systems.

As AI-generated music becomes more common across advertising, creator platforms and digital media, the industry’s focus is shifting beyond generation alone. Questions around licensing, ownership and compensation are increasingly shaping how AI music companies position themselves and build trust with creators.

Votee AI, an enterprise AI company headquartered in Hong Kong, has partnered with its Toronto-based research lab Beever AI to launch Beever Atlas. The new platform is designed to turn workplace chats into searchable knowledge that AI systems can retrieve and understand.

The release focuses on a growing issue inside organisations. Much of today’s workplace knowledge now exists inside chat platforms such as Slack, Microsoft Teams, Discord and Telegram. Important discussions, project decisions and technical information often disappear into long message histories that are difficult to search later.

Beever AI developed the platform to organise those conversations into a structured system for AI assistants. The software connects with Telegram, Discord, Mattermost, Microsoft Teams and Slack, then converts conversations into linked records of people, projects, files and decisions.

The collaboration combines Votee AI’s enterprise infrastructure work with Beever AI’s research around AI memory systems. The companies are releasing two versions of the product. The open-source edition is aimed at individual developers, researchers and creators. The enterprise edition is designed for banks, government agencies and larger organisations with stricter security requirements.

The release also reflects a broader shift happening across the AI industry. Companies are increasingly looking at how AI systems store and retrieve long-term knowledge, rather than relying solely on large context windows or search-based retrieval.

Earlier this year, OpenAI founding member and former director of AI at Tesla  Andrej Karpathy discussed the growing need for what he described as “LLM Knowledge Bases.” He argued that AI systems need structured and evolving memory rather than depending only on context windows and vector search.

Beever Atlas approaches that problem through workplace communication. Instead of focusing mainly on uploaded files, the system is designed around conversations that happen daily across team chat platforms. It can also process images, PDFs, voice notes and video files within the same searchable system.

The companies say the software is designed to work directly with AI assistants and coding tools such as Cursor, AWS Kiro and Qwen Code. Integrations for OpenClaw and Hermes Agent are expected later in 2026.

Pak-Sun Ting, Co-Founder and CEO of Votee AI  said: "Hong Kong has always been known for property and finance. Beever Atlas is proof that world-class AI infrastructure can emerge from an HK-headquartered company and be shared openly with the world. Every growing organization faces the same silent liability: conversational knowledge loss. Beever Atlas turns this perishable resource into a compounding organizational asset."

A large part of the enterprise version focuses on privacy and access control. The system mirrors permissions from Slack and Microsoft Teams so users can only retrieve information they are already authorised to access. Permission updates are reflected automatically when access changes inside company systems.

The enterprise edition also includes audit logs, encryption controls and data retention settings for organisations handling sensitive internal data. Companies can run the software entirely inside their own infrastructure using Docker and connect it to their preferred AI models through LiteLLM.

The companies argue that organising information is more useful than simply storing chat archives. Jacky Chan Co-Founder and CTO of Votee AI said: "The key technical decision was to treat agent memory as a knowledge engineering problem, not a retrieval problem. Structure beats similarity — a typed graph of who works on what is more useful to an AI than vector search over a Slack archive."

The software also includes protections against prompt injection attacks and systems designed to reduce hallucinated responses. According to the companies, the AI is designed to return “I don't know” with citations when confidence is low instead of generating unsupported answers.

As workplace communication becomes increasingly fragmented across chat platforms, companies are beginning to treat internal conversations as information that AI systems can organise, retrieve and build on. Beever Atlas reflects a broader push to turn everyday workplace communication into long-term organisational memory.

Across Hong Kong’s public universities, entrepreneurship is now part of the campus ecosystem. Many universities offer startup funding, mentorship, training, workspace, investor access and pathways into larger incubation programmes such as Hong Kong Science and Technology Park (HKSTP) and Cyberport.

For student founders, researchers and alumni, this can be a useful place to begin. You may be able to test an idea, build a prototype, form a company or apply for early funding through your own university before looking for external investors.  

The challenge is knowing where to start. Each university has its own startup programmes, eligibility rules and funding structure. Some are designed for student ideas. Others are built for research commercialization, deep tech ventures or startups already preparing to raise investment. Below is a practical guide to startup support and university startup funding at five major publicly funded universities in Hong Kong.

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The University of Hong Kong (HKU): Startup support for student founders, deep tech and research commercialization

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HKU offers a wide range of entrepreneurship support through HKU Techno-Entrepreneurship Core, also known as HKU TEC. Its programmes cover early ideas, deep tech projects, Greater Bay Area (GBA) expansion, research commercialization and investor matching.

HKU is especially relevant for founders working with university research, intellectual property or technology-led business ideas. It also has entry-level support for students and graduates who are still testing an idea.

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Best fit: HKU works well for student founders, researchers and alumni who want a structured route from idea stage to technology commercialization.

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City University of Hong Kong (CityUHK): HK Tech 300 and a clear startup pathway

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CityUHK’s main startup platform is HK Tech 300. It is one of the clearest university startup pathways in Hong Kong because it is built in stages: training, seed funding, angel investment and access to external funding.

The programme is open to CityUHK students, alumni, research staff and members of the public using CityUHK intellectual property or technology.

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Best fit: CityUHK is a strong choice for founders who want a step-by-step startup journey with clear funding stages.

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Hong Kong University of Science and Technology (HKUST): Startup funding for tech founders and research teams

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HKUST has a broad startup ecosystem with support for students, alumni, researchers and faculty. Its entrepreneurship pathway covers idea exploration, prototyping, MVP testing, research commercialization and investment.

The university’s startup support is especially strong for technology companies, deep tech projects and teams commercialising HKUST research.

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Best fit: HKUST is especially useful for tech startups, deep tech teams and founders who need a route from prototype to commercialization.

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The Hong Kong Polytechnic University (PolyU): Startup funding for product, applied research and GBA expansion

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PolyU’s startup support is practical and product-focused. Its programmes cover early ideas, seed-stage teams, Greater Bay Area expansion, translational research and investment.

This makes PolyU a good fit for founders working on engineering, hardware, applied technology, social impact or commercialization of university research.

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Best fit: PolyU is well suited for product-led startups, applied technology projects, GBA expansion and founders who want industry-facing support.

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The Chinese University of Hong Kong (CUHK): Startup support from idea stage to technology commercialization

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CUHK offers support for student founders, researchers and alumni through the Pi Centre and the Knowledge Transfer Office. Its ecosystem covers pre-incubation, TSSSU funding, early translational research, social impact projects and Greater Bay Area entrepreneurship.

CUHK is especially useful for students who want to start with an idea and later move into funding, mentorship or external incubation.

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Best fit: CUHK is a good starting point for student founders who need pre-incubation support, and for researchers moving early-stage ideas toward commercial use.

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Which Hong Kong university startup programme should you choose?

There is no single best programme for every founder. The right choice depends on your stage, your university connection and the type of startup you are building.

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The bottom line

Hong Kong’s university startup ecosystem is bigger than many founders realize. If you are a student, alumnus, researcher or university-linked founder, your campus may already offer a route into funding, mentorship, workspace and incubation.

The key is to choose a programme that matches your current stage. Some founders should start with idea validation. Others may be ready for seed funding, TSSSU support or investment.

Before applying, check the latest deadline and eligibility rules on the official university page. These programmes change often, and some funding rounds open only once or twice a year.

A new survey from Povaddo, a policy research firm, suggests that concern about artificial intelligence is no longer limited to industry or academia. It is now firmly present within the policy community.

The survey draws on responses from 301 public policy professionals across the United States and Europe, including lawmakers, staffers and analysts involved in shaping and evaluating public policy. A majority of respondents—61%—say governments are falling short in addressing the negative impacts of AI.

There is also broad agreement that regulation needs to increase. In the United States, 92% of respondents support stronger AI regulation, compared to 70% in Europe. At a time when consensus is often difficult, the findings point to a shared view across policy circles that current frameworks are not keeping pace with technological development.

Differences emerge when looking at how AI is affecting national contexts. In the U.S., 57% of policy experts believe AI is already harming the labor market. In Europe, 34% say the same. U.S. respondents are also more likely to see AI as a greater threat to jobs than immigration, with 63% holding that view compared to 47% in Europe.

On misinformation, responses are closely aligned. A large majority of policy experts in both regions expect an AI-driven misinformation crisis within the next one to two years—87% in the U.S. and 82% in Europe. Many also believe that AI-generated or AI-amplified misinformation could affect elections and public health information.

Some respondents frame the risks in more fundamental terms. In the United States, 41% of policy experts say AI poses an existential threat to humanity. In Europe, 29% share that view. U.S. respondents are also more likely to believe that advances in AI could harm global security and stability.

The findings come as policymakers begin to respond more actively. In the U.S., Senators Josh Hawley, Richard Blumenthal and Mark Warner have introduced bipartisan legislation focused on AI accountability, including measures aimed at protecting workers and children.

In Europe, the introduction of the EU AI Act marks a more advanced regulatory approach. The framework sets out rules based on levels of risk and is widely seen as the first comprehensive attempt to govern AI at scale.

William Stewart, President and Founder of Povaddo, said: "What makes these findings so significant is who is saying it. These are the practitioners who work inside the policy process every day, spanning every corner of the policy world from defense to healthcare to finance, not activists or everyday citizens. These findings foreshadow real action. The current path of governments accelerating AI deployment while falling short on governance is not sustainable, and the people who know that best are the ones in this survey. You cannot have nine-in-ten policy insiders demanding more regulation and four-in-ten calling AI an existential threat without that eventually moving the needle in Washington and Brussels in terms of legislative or regulatory action".

Taken together, the survey reflects a shift in how AI is being discussed within policymaking circles. Concern is no longer limited to future risks. It is increasingly tied to current gaps in governance and the pace of deployment.