cross-posted from: https://scribe.disroot.org/post/11960652
At first glance, China appears well placed to dominate the deployment of artificial intelligence on the factory floor. The country is the world’s manufacturing powerhouse. Its AI systems are close to matching the performance of leading US rivals. And the leadership in Beijing has made industrial AI a national priority to boost economic growth.
Europe has other ideas. The continent’s old industrial conglomerates are also trying to harness AI so they can dominate the next phase of factory automation. With an ocean of production data to draw upon, companies such as Siemens, ABB and Schneider Electric are hoping the continent can corner this lucrative area of AI development after failing to produce world-leading chatbots like DeepSeek or Claude.
Success would allow those venerable companies to supply the next generation of data-driven factories and make it harder for upstart, tech-focused rivals to steal their customers. It would also help to limit Europe’s technological and economic dependence on the US and China.
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China produces around 30% of the world’s goods, worth $4.7 trillion a year, and Beijing has vowed to use AI to modernize the country’s manufacturing base. Chinese companies installed almost 60% of the world’s new industrial robots last year, according to the International Federation of Robotics, and the government is prioritizing the wide-scale deployment of AI with its “AI + manufacturing” program.
But China’s role as the world’s workshop is a relatively recent phenomenon, the quality of its factories varies widely and many facilities still rely heavily on manual labor. Europe has been installing robots and digitizing manufacturing for longer because it was a way to address the region’s relatively high labor costs. And a lot of the equipment installed in factories worldwide was made in Europe. Germany’s Siemens, for example, says it supplied one in every three factory controls deployed across the globe.
This means European companies have a lot of the data that’s needed to build the best AI-driven industrial systems. They are particularly adept at using it to predict equipment failures, improve product quality and make existing machinery more efficient, according to Antonia Hmaidi of the Mercator Institute for China Studies.
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