SAP CEO Claps Back at Nvidia’s AI Infrastructure Dominance—’Not So Fast’

SAP’s CEO just threw shade at Nvidia’s aggressive AI infrastructure expansion—calling it overhyped and questioning its long-term viability. Here’s why the tech giant isn’t buying the hype.

The AI Arms Race Heats Up

While Nvidia races to cement its AI empire, SAP’s leadership isn’t convinced. The enterprise software titan argues that real-world scalability—not just silicon—will decide the winner.

A Cynical Finance Twist

Wall Street’s already pricing Nvidia like it’ll single-handedly power the AI revolution. But if SAP’s skepticism spreads? Those valuations might look as inflated as a 2021 meme coin.

SAP steps back from gigafactory ambitions

Klein’s statements indicate a drastic change in SAP’s AI strategy. Just six months ago, he said he fully supported Europe building its own AI “gigafactories” — massive facilities for training and scaling AI models. At the World Economic Forum in Davos in January, he cited the US-led “Stargate” effort as a “great role model” for Europe. He vowed to “absolutely support” a similar European effort at the time.

SAP, behind the scenes, had been in discussions with other German companies to make a joint investment in one of a series of European Union-backed AI gigafactories. But the discussions eventually broke down. In the words of SAP’s head of customer services and delivery, Thomas Saueressig, it turned “into nothing” because of divergent visions and a short-term view.

This week, SAP clarified that it is no longer interested in investing in or participating operationally in such mega-projects. Instead, it wants to back such initiatives as a tech and software provider, supplying tools, platforms, and customisable AI applications for those doing infrastructure.

Earlier this year, the European Union committed to spending 20 billion euros ($23 billion) to create five AI gigafactories. If the ambition is clear, SAP’s retreat to the sidelines underscores an increasing skepticism within Europe’s industrial leaders about trying to copy the American model of AI supremacy.

Developers embrace open-source AI for faster innovation

Klein also wondered about the value of racing in the long term to train huge proprietary models of AI. Instead, he sees a MOVE toward cheaper, open-source alternatives. He cited as an example the Chinese company DeepSeek, which recently made headlines for besting the top US AI companies by using a model constructed on open-source code trained at a fraction of the cost.

Klein said the success of open-source models demonstrates that it isn’t necessary to have unlimited computing power or billion-dollar budgets to develop effective AI solutions. He emphasized that what matters are intelligent, streamlined models that are relevant to the market and function well in real business environments.

He proposed that the days of monolithic AI infrastructure are waning. Now that models are mainstream, the significant issue will be how effectively companies apply these tools to real-world problems.

SAP’s priority is to integrate AI capabilities into enterprise software systems. Klein has written about AI-augmented workflows for logistics, predictive maintenance for factory equipment, intelligent customer service bots, and real-time decision-making dashboards for supply chain managers.

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