Code, Capital, and Clusters: Understanding Firm Performance in the UK AI Economy

The UK has established a distinctive position in the global AI landscape, driven by rapid firm formation and strategic investment. However, the interplay between AI specialisation, local socio-economic conditions, and firm performance remains under-explored. This study analyses a comprehensive dataset of UK AI entities (2000–2024) combining data from Companies House, ONS, and glass.ai. We find a strong geographical concentration in London (41.3\% of entities) and technology-centric sectors, with general financial services reporting the highest mean operating revenue. Firm size and AI specialisation intensity are primary revenue drivers, while local factors—Level 3 qualification rates, population density, and employment levels—provide significant marginal contributions, highlighting the dependence of AI growth on regional socio-economic ecosystems. The forecasting models project sectoral expansion to 2030, estimating 4,651 [4,323–4,979, 95\% CI] total entities but also a rising dissolution ratio (2.21\% [-0.17–4.60\%]), indicating a transition toward slower sector expansion and consolidation. These results provide robust evidence for place-sensitive policy interventions, such as cultivating regional AI capabilities beyond London to mitigate systemic risks; distinguishing between support for scaling (addressing capital gaps) and deepening technical specialisation; and strategically shaping ecosystem consolidation. Targeted actions are essential to foster both aggregate AI growth and balanced regional development, transforming consolidation into sustained competitive advantage.