Focus The global AI divide By Meng Khong Tong, CEO, SotaTek US
Why divide matters In the next phase of AI development infl uence won’t come just from invention, but from integration. Nations that can weave AI into their industries, governance and infrastructure will gain outsized economic and strategic leverage. For Western policymakers, this raises
critical questions. What strategic advantage does semiconductor leadership provide if the applications built on top of that infrastructure and the data fl ows that sustain them are dominated elsewhere? Can a country truly lead in AI if its citizens and businesses rely on foreign-built applications to interact with it? For enterprises, the question is equally
T
he global race for artifi cial intelligence (AI) dominance is no longer about who can build the biggest models, but
rather who can deploy them faster, smarter and at scale. While the US continues to be a leader in semiconductor innovation and the underlying infrastructure for model training and inference, China has quietly closed the gap in application and deployment. Its developers are rolling out AI powered tools, platforms and consumer experiences at a pace that few Western fi rms can match. T is divergence marks a turning point in
global technology competition. For the fi rst time, innovation leadership is fragmented with one power commanding the core technology stack and the other mastering its use. T e implications of this split are not merely technical – they are economic, strategic and geopolitical.
Who owns the foundations? T e US holds a commanding advantage in foundational technologies. From Nvidia’s chips to OpenAI’s models and the hyperscale infrastructure built by fi rms like Amazon, Google and Microsoſt , the US remains the centre of AI research and model development. But these strengths come with a caveat: foundational innovation is capital intensive, slow moving and subject to supply chain vulnerabilities. Training a cutting-edge AI model can require months of computing
time, billions of dollars in hardware and access to specialised semiconductors – resources few companies and even fewer nations can aff ord. T is has leſt the US with a “top-heavy”
AI advantage: powerful at the core, but less agile in execution. T e very structures that make American AI advanced, the layers of governance, compliance and ethics, can also slow down experimentation and deployment.
China’s quiet edge China’s AI ecosystem operates diff erently. Its developers, backed by strong domestic demand and an open mandate for experimentation, are pushing out AI applications at a remarkable rate. From retail automation and logistics optimisation to generative AI-powered e-commerce and education tools, Chinese enterprises are embedding AI into daily life at scale. T is approach is less about achieving
technological purity and more about velocity and iteration. Chinese companies test products in real markets, gather user feedback and refi ne rapidly, a cycle that turns learning into a competitive advantage. In AI, the model that reaches the user fi rst oſt en wins the data, and gathers more speed. T e result is a widening gap between where AI innovation happens and where AI adoption happens. T e US may defi ne the science, but China is defi ning the market.
08 February 2026
www.electronicsworld.co.uk
urgent. Competing in this environment will require a balance between technological rigour and deployment speed. Companies that cling too tightly to the perfectionist model of innovation may fi nd themselves outpaced by competitors who move faster.
A multipolar AI future? We are entering a multipolar AI era, where no single nation will own the full stack of innovation. T e US will likely remain the hub for advanced semiconductors, model research and cloud infrastructure. China will continue to dominate in user facing applications and rapid commercialisation. Europe, Southeast Asia and India will emerge as critical nodes for regulation, data governance and region-specifi c adaptation. For businesses, the path forward is clear:
build strategies that acknowledge this interdependence. T at means diversifying supply chains, understanding the regulatory nuances across markets and investing not only in AI R&D but in AI readiness, the people, processes and systems that turn innovation into execution. In this new landscape, the winners will not be those with the largest models, but those with the broadest reach. AI leadership will belong to the players who can bridge the gap between technological capability and practical impact. T e age of AI hegemony is over. T e age of AI ecosystems has begun.
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