SAN FRANCISCO, Oct. 27, 2026 /PRNewswire/ -- Asia's semiconductor expertise, manufacturing infrastructure and industrial supply chains could give the region a significant role in the development of Physical artificial intelligence (Physical AI), as investors look beyond software to the challenges of deploying intelligent machines in real-world environments.

Speaking at the recently concluded Silicon to Scale: A Physical AI Summit during SF Tech Week, venture capital investors highlighted opportunities for Taiwan, Japan and Southeast Asia as robotics companies move from prototypes toward commercial deployment.

Co-hosted by Startup Island TAIWAN Silicon Valley Hub and SparkLabs Taiwan, also part of SparkLabs Taiwan's US Landing Program, the summit drew more than 170 investors, industry leaders and founders, and featured five Taiwanese Physical AI startups: Morale AI, MetAI, Jmem Tek, Neuroshine and APMIC.

The Venture Dialogue, 'What Makes Physical AI Investable?', was moderated by Bernard Moon, Co-Founder and Managing Partner of SparkLabs Group, with Jeff Tao, Director of AI Infrastructure at Citi; Dr. Vandon Duong, Principal at DCVC; and Kosuke Yusa, GP and Head of Investment at Open Network Lab Fund, DG Incubation, Inc.

Before the venture dialogue, the summit opened with a Corporate Dialogue, 'From Concept to Production: Physical AI at Scale,' moderated by James Lim of NVIDIA's Venture Capital Strategy & Partnerships team. Panelists Austin Tseng, President of Delta Electronics (Americas); Vince Fong, Co-Founder and CEO of Highrise AI; and Daniel Yu, Co-Founder and CEO of MetAI, discussed what it takes to move Physical AI from simulation to real-world deployment, from closing the sim-to-real gap to the compute, edge and energy infrastructure needed to scale.

"Taiwan's semiconductor and manufacturing strengths are well known in Silicon Valley, and investors here are increasingly keen to partner with Asian strategic investors as Physical AI moves from prototype to production," said Edgar Chiu, Founding Managing Partner of SparkLabs Taiwan. "Our mission is to be that bridge, bringing Taiwanese founders to US customers and capital, and connecting US companies with Taiwan's supply chain and strategic investors."

For Taiwan, the shift could create opportunities to leverage its strengths in semiconductors, electronics and advanced manufacturing, according to Uly Su, director and head of Startup Island TAIWAN Silicon Valley Hub.

Su said the hub is organizing a series of industry dialogues and investor gatherings to connect Taiwanese startups with Silicon Valley's technology and investment communities. The initiatives aim to help founders identify commercial opportunities, build international partnerships and strengthen Taiwan's position in the emerging Physical AI industry.

Asia's manufacturing advantage

Jeff Tao, Director, AI Infrastructure at Citi said Asia's established semiconductor and electronics supply chains could provide a foundation for the growth of Physical AI, particularly as companies seek to diversify production amid geopolitical uncertainty.

He pointed to semiconductor assembly and testing operations in Southeast Asia, including Malaysia, as examples of existing manufacturing capabilities that could support emerging technologies.

Taiwan, meanwhile, occupies an important position in the global semiconductor industry, providing expertise and manufacturing infrastructure relevant to the development of AI-powered robotics.

Tao said Physical AI companies could benefit from combining Asia's manufacturing strengths with access to the United States, where high labor costs are creating opportunities for industrial automation.

Kosuke Yusa, GP & Head of Investment, Open Network Lab Fund, DG Incubation, Inc.said Japan offers a different advantage: industrial environments that demand flexibility and continuous improvement.

Japanese production lines frequently change, requiring robotics systems that can adapt to different tasks and operating conditions, he said.

The missing piece: Real-world data

Despite Asia's manufacturing capabilities, investors identified access to training data as a major obstacle to the commercialization of Physical AI.

Unlike large language models, which can draw on extensive digital information, robotics systems require specialized datasets collected from factories, production lines and other physical environments.

Tao said collecting such information is particularly difficult because manufacturers are reluctant to share proprietary operational data.

Dr. Vandon Duong Principal at DCVC (Data Collective Venture Capital) said AI developers are willing to pay substantial amounts for diverse, well-annotated datasets, creating opportunities for startups specializing in data collection.

However, he questioned how companies could build lasting competitive advantages as more businesses enter the market.

Computing costs and investment opportunities

The panel also discussed rising AI infrastructure spending and the constraints it creates for emerging companies.

Tao said electricity availability has become an increasingly important bottleneck for AI infrastructure development in the United States, following earlier constraints involving GPUs and data centers.

He identified opportunities for startups developing more efficient power systems, cooling technologies and semiconductor manufacturing processes.

Duong said investors are also examining technologies that reduce inference costs and improve computing efficiency.

From Taiwan to Silicon Valley

The discussion underscored the potential for Asian Physical AI startups to combine regional manufacturing capabilities with American capital, customers and technology markets.

Investors said founders must understand the requirements of industrial customers, particularly when developing systems for manufacturing environments where reliability and adaptability are critical.

While panelists acknowledged concerns about AI valuations and infrastructure costs, they remained optimistic about long-term adoption.

Tao also cautioned that productivity gains from AI could create short-term labor displacement, raising broader questions about how the economic benefits of automation will be distributed.