From generative AI to green energy, 15 finalist teams will take the stage after a record 390 teams entered this year's competition.
Updated
October 2, 2026 9:13 AM
Entrance Piazza, Hong Kong University of Science and Technology. PHOTO: HKUST
The HKUST-Sino Million Dollar Entrepreneurship Competition 2026 will hold its finals on October 12, following a record 390 teams from 24 countries and regions taking part in this year's competition.
Jointly organised by the Hong Kong University of Science and Technology (HKUST) and Sino Group, the competition aims to give young entrepreneurs a platform to develop and present their ideas. Sino Group Deputy Chief Executive Officer Wong Wing-lung said the Group has partnered with HKUST for many years to support young talent and the development of innovation and technology in Hong Kong.
He said the competition gives young entrepreneurs a platform to showcase their creativity, exchange ideas and put their concepts into practice.
The international participation is reflected in the universities and regions represented this year. Participants include students from the University of Oxford in the UK, the University of Pennsylvania in the US and the National University of Singapore. Teams have also joined from Australia and South Africa.
After multiple rounds of assessment, 15 teams have advanced to the finals. Their projects cover health technology, generative AI, smart buildings, and green energy management. At the finals, the teams will present their solutions to a judging panel comprising venture capital investors, industry leaders and academic experts. They will compete for more than HK$1 million in prizes and awards.
The 2026 competition has also expanded the areas it recognises. Three new awards have been introduced this year: the Transformative AI Award, the NextGen Biotech Award and the Societal Influential Award. The competition will also continue to offer the Sustainability Impact Award. Together, the awards focus on areas ranging from emerging technologies to social and sustainability challenges.
The organisers are also adding new activities to help teams develop their ideas further. This year's programme includes pitching skills training, AI workshops, investor matching sessions and mentorship opportunities. The AI workshops will cover areas including generative AI and AI agents, while representatives from venture capital firms such as Gobi Partners and InnoAngel Fund have been invited to share insights on fundraising, product positioning and market expansion.
These activities are intended to help young entrepreneurs strengthen their business plans and presentation skills. They also give teams opportunities to explore technology commercialisation and potential market applications.
The competition will also launch the InnoBay 1M PLUS Program during the Grand Final. The programme will focus initially on Smart City Development and aims to connect HKUST start-ups and competition alumni with industry partners. It will also support potential proof-of-concept opportunities and help promising innovations move towards commercial adoption.
The Grand Final will also include an audience voting segment. Members of the public will be able to vote for the team they believe has the greatest potential and impact, with those who correctly vote for an eventual winning team entering a lucky draw.
For Wong, the competition is part of a broader effort to develop Hong Kong's innovation and technology talent. He noted that Hong Kong's first Five-Year Plan and latest Policy Address both highlight innovation, technology and talent development.
Prof. Tim Cheng Kwang-Ting, HKUST's Vice-President for Research and Development, said the competition has also served as a platform for aspiring entrepreneurs to connect with investors, understand market needs, test their ideas and gain practical entrepreneurial experience. He added that the competition has strengthened its training, mentorship and networking activities this year.
The Group believes that closer cooperation between industry, academia and research institutions can help turn innovative ideas into practical applications. Through the competition and its wider support programmes, young entrepreneurs are given opportunities to develop their ideas, connect with potential partners and explore how they can be taken from early concepts towards real-world applications.
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AI growth is increasingly becoming a manufacturing, packaging and deployment challenge — not just a computing one.
Updated
August 10, 2026 5:56 PM
Taipei 101 and Taipei Nan Shan Plaza, viewed from Elephant Mountain. PHOTO: UNSPLASH
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.