Europe is falling behind in the AI race. U.S. private investment in AI rose to $285.9 billion in 2025, according to Stanford University figures, dwarfing the $20.9 billion invested in Europe. The U.S. has higher AI adoption rates, a greater number of data centers, and the most valuable AI companies in the world.<\/p>\n
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In total, eight U.S. tech companies have surpassed trillion-dollar valuations, with OpenAI and Anthropic poised to join their ranks. Currently, the closest Europe comes to a trillion-dollar business is ASML, which has a market cap of $670 billion.<\/p>\n
Europe\u2019s struggle to build a trillion-dollar market-cap company has been on the mind of Peter Koerte, CEO of Siemens\u2019s Smart Infrastructure division\u2014the most profitable of Siemens\u2019s four core businesses. \u201cWe have really smart people in Europe,\u201d he says. \u201cBut the U.S. and China have a massive advantage because of their huge domestic markets. In Europe, it\u2019s disproportionately harder to scale because of the different languages, regimes, and political systems.\u201d<\/p>\n
Europe\u2019s industrial data advantage<\/h2>\n
One place where Europe could have an upper hand, he says, is in the development of AI for industrial processes. Siemens\u2019s Industrial Foundation Model, which is still in development, has the potential to shorten engineering cycles by up to 40%, according to Koerte, and he foresees use cases in the automotive and aerospace industries. Siemens plans to invest more than \u20ac1 billion in industrial AI over the next three years.<\/p>\n
Unlike LLMs, which are primarily trained on language, industrial models are taught on manufacturing and engineering data for use in those fields. \u201cIn engineering, you need to be precise. If the AI hallucinates or makes up a calculation, you will have a problem,\u201d says Koerte. \u201cWhen we try to use large language models in engineering and production, it doesn\u2019t work\u2014words are much more imprecise.\u201d<\/p>\n
But securing sufficient training data for industrial models can represent a challenge. Koerte says this is where the longevity of Europe\u2019s leading manufacturers could be an advantage. More than half of the companies on the Fortune 500 Europe are over 100 years old. \u201cWe have the largest install base. All the data is available within the pharma, automotive, and chemical companies that have been operating in Europe for decades,\u201d Koerte says.<\/p>\n
Persuading these companies to part with their data may prove more difficult. \u201cWe\u2019ve been very good at educating organizations that data is the new oil,\u201d Koerte says. \u201cBMW or AstraZeneca is not going to put their data on the internet, so they need to trust others to train these models using their data.\u201d<\/p>\n
This is where Koerte believes Siemens, which was founded in 1847, has another edge. \u201cWe have decades-old relationships with most of the companies we work with,\u201d he says. \u201cThey trust us to take utmost care of their data and can rely on us if something breaks.\u201d<\/p>\n
So far, Siemens has managed to persuade companies to share their data in return for access to its AI model and the ability to request new use cases. The data is used only for training, and ownership is retained by the company that created it, Koerte explains.<\/p>\n
Talent, partnerships, and the limits of sovereignty<\/h2>\n
The other issue is talent. U.S. institutions employ 59% of the world\u2019s elite AI researchers, according to research platform MacroPolo Archive\u2019s Global AI Talent Tracker. But last year, Siemens poached Amazon\u2019s vice president of generative AI, Vasi Philomin, to be its head of data and AI. Manu Parbhakar, a former Amazon Web Services director, also joined Siemens to lead its Silicon Valley\u2013based strategy and partnerships team. \u201cYou have to write a bigger check than you usually would in Europe,\u201d Koerte says. \u201cBut you need to have the experts to build this.\u201d U.S. partnerships have also been crucial. Siemens is currently working with Nvidia to build an industrial AI operating system.<\/p>\n
Siemens is not the only company attempting to apply AI to physical challenges. Amazon founder Jeff Bezos\u2019s latest startup Prometheus is using AI to assist engineers in the design and manufacturing of a range of devices\u2014from computers to automobiles to jet engines. \u201cSpeed is of the essence. But I think we are in a good position,\u201d Koerte says.<\/p>\n
Siemens\u2019s tighter ties with the U.S. come at the same time the European Commission is pushing for a split from American Big Tech. The EU relies on non-EU countries for 80% of its digital infrastructure and services, and the Commission\u2019s tech sovereignty package is aimed at supporting the creation of homegrown alternatives.<\/p>\n
Although Koerte believes Europe needs to develop greater AI capabilities, complete sovereignty remains a pipe dream. \u201cTo put it simply, there\u2019s no sovereignty. Not in the U.S. Not in China. Not in Europe,\u201d he says. No country has all the resources required to develop AI. \u201cIf you want to create high-performance chips, you need the ultraviolet machines made by ASML, in the Netherlands. It sources many parts from German companies, and those businesses\u2019 suppliers are spread across the world,\u201d Koerte adds.<\/p>\n
Siemens CEO Roland Busch has warned that a \u201cdigital iron curtain\u201d risks slowing technological progress as governments seek greater control over digital supply chains. Rather than pursuing technological isolationism, Koerte advocates greater collaboration across borders. Only through such cooperation, he argues, can we fully realize AI\u2019s promised efficiency gains.<\/p>\n<\/div>\n
#Europes #industrial #legacy #give #advantage<\/p>\n","protected":false},"excerpt":{"rendered":"
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<\/p>\n