Conclusion and lessons for Europe
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Are there any lessons that Europe and the UK could adapt from China’s approach to developing industrial AI ecosystems? After all, Chinese provinces are governed like large socioeconomic entities with populations and economic outputs that surpass some European countries, and it is often said that a province is to China what a country is to Europe.

One starting point is to shift the focus toward clusters outside of Berlin, London and Paris, in order to find the dark horse AI ecosystems that could facilitate Europe-wide diffusion of AI technologies. These ecosystems cannot be conjured from thin air. One PhD dissertation on S&T parks found that parks built in areas with a weak local technological base resulted in a glorified real estate project that was disconnected from the local economy. The ecosystems in Hangzhou and Hefei provide some additional guidance. In both cases, there is a key ‘anchor tenant’ (iFlytek for Hefei and Alibaba for Hangzhou) and elite university (USTC for Hefei and Zhejiang University for Alibaba) that glue the ecosystem together. Empirical analysis has shown that the presence of a large technology firm can enhance the productivity of local innovation systems by enabling local university research.

With an eye towards attracting the global and extra-regional linkages that are necessary to build competitive clusters in a world of globalised innovation, ambitious policymakers could provide increased access to capital and other policy supports to AI ecosystems in the making in cities such as Amsterdam, Barcelona and Stockholm. In the United Kingdom, outside of London, other important clusters are forming in Cambridge, Manchester, Oxford and elsewhere. Already a leading hub for advanced materials, Manchester could have the ingredients to build a dark horse AI ecosystem around the University of Manchester and the establishment of a government communications headquarters as anchors.

Finally, in line with Hefei’s approach, European governments should better assess opportunities to specialise in specific AI subdomains or parts of the value chain. There is no need to reinvent the wheel. Manchester, for instance, has adapted the national Artificial Intelligence and Data Grand Challenge, which is focused on health care applications, toward local advantages in smart manufacturing and cybersecurity. The European Commission’s Key Enabling Technologies strategy has examined European comparative advantages in six technology groups: micro and nanoelectronics, nanotechnology, industrial biotechnology, advanced materials, photonics, and advanced manufacturing technologies. Given its potential to enable all six of these enabling technologies, incorporating AI into the Key Enabling Technologies framework could help sustain the competitiveness of Europe’s industrial base.


Jeffrey Ding

DPhil researcher and China lead at the Centre for the Governance of AI, University of Oxford’s Future of Humanity Institute