The Role of Middle Powers in the Age of AI

The global AI race is increasingly defined by asymmetry across the stack. The US remains dominant in frontier models, capital and hyperscale infrastructure, while China combines growing model strength with scale in patents, publications and open-model diffusion. Stanford’s 2025 AI Index reported that US-based institutions produced 40 notable models in 2024 versus 15 in China and 3 in Europe, while US private AI investment reached $109.1 billion compared with $9.3 billion in China and $4.5 billion in the UK. At the same time, compute is becoming a strategic bottleneck in its own right, with the OECD noting that the physical location of AI compute matters for latency, regulation, access and economic spillovers. For middle powers, then, the question is not how to outspend Washington or Beijing, but how to secure leverage in selected layers of the stack and avoid forms of dependence that cannot easily be reversed. 

This panel aims to examine an important shift in where value is created: not only in training frontier models, but in the intermediary layer of distillation, fine-tuning, evaluation, inference optimisation, systems integration, benchmark-setting and domain applications. Middle powers are increasingly seeking not to attempt replication of the frontier models that are driving global technological competition and capital concentration in the US and China, but to build open ecosystems, sectoral data assets, trusted regulatory environments and high-value applications suited to their own talent base, power resources, public-sector scale and digital maturity.