The AI advantage is moving into the physical economy

The AI advantage is moving into the physical economy

Industrial ecosystems will be more important in the next phase of artificial intelligence

For the past few years, the easiest answer to the question “where should an AI start-up build?” was wherever the leading models, research talent and computing infrastructure were concentrated. That made sense when the AI economy was primarily associated with software. But as AI expands into the physical world, via robots, factories, energy systems and infrastructure, the geography of start-up advantage is becoming more complicated.

Access to powerful models will remain important. Yet for companies building AI systems that operate in the physical world, models are only one part of the equation. These start-ups must also prototype, test, manufacture and deploy machines and devices. That requires components, engineering expertise, production partners, regulatory knowledge and customers willing to test systems in real operating environments.

Industrial ecosystems may therefore become more important to the next phase of AI than conventional start-up rankings suggest.

Taiwan offers a strong example. It ranked 20th overall in StartupBlink’s latest Global Startup Ecosystem Index, but placed seventh in Hardware and Internet of Things and 16th in AI. This reveals a degree of specialisation that its overall position may obscure.

Taiwan’s advantage is not simply its AI start-ups or semiconductor expertise. Its technology ecosystem is connected to one of the world’s deepest hardware and manufacturing bases. Chipmaker TSMC is the most visible anchor, but the broader asset is its surrounding network: component suppliers, precision manufacturers, electronics companies and engineers experienced in turning technical designs into physical products.

That specialisation matters because the sources of competitive advantage change as AI moves from screens into machines. In software AI, value and investment have become concentrated around a small number of frontier model companies. OpenAI, Anthropic and Google have developed general-purpose systems that support a wide range of products. Capital has followed, with 61 per cent of global venture capital funding deployed into AI firms in 2025, according to OECD analysis of Preqin data.

Software start-ups can still build durable businesses around proprietary data, specialised workflows, distribution and industry knowledge. But those offering only a thin interface on top of another company’s model may find it increasingly difficult to differentiate themselves.

Physical AI creates a different competitive landscape. Because these systems must combine software with hardware, manufacturing and real-world deployment, physical AI is a collection of specialised markets. A warehouse robot, an agricultural sensor system and an electricity grid controller may all use AI, but they operate on different hardware, face different regulations and serve different buyers.

Only a few ecosystems can assemble the research density, computing infrastructure and capital required to compete directly in frontier models. Physical AI draws on assets that more regions already possess: manufacturing capacity, technical universities, logistics networks, corporate customers and sector-specific expertise.

The goal is not to replicate Silicon Valley across the entire AI economy. It is to identify where an ecosystem’s existing capabilities can support a defensible part of the physical AI value chain. The better question is: which part of the physical AI economy can an ecosystem help start-ups build, test and scale better than anywhere else?

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