Samsung Co-Leads $231M Bet to Break Nvidia's AI Chip Grip

For market participants, a 200-million-euro Series A into a 2024-founded chip startup is more than a venture deal — it's a read on Nvidia's competitive moat. Samsung's rare co-lead investment alongside EQT-backed capital shows that the incumbent memory and foundry ecosystem is hedging, and that inference workloads may have different silicon economics than training.

The pivotal development is Samsung's decision to co-lead a 200-million-euro, or $231 million, Series A investment in Euclyd, a Dutch startup founded in 2024 that is designing an AI chip system for inference with a fundamentally different architecture than Nvidia's graphics processing units. The round, disclosed by CNBC on September 14, 2026, also drew co-lead backing from Somerset Capital Partners, the EQT-managed Scaleup Europe Fund, and Innovation Industries. The size is extraordinary for a Series A, especially for a company that has not yet proven its silicon in commercial deployments, and it reflects a broader surge in capital flowing into alternatives to Nvidia's dominant AI compute stack.

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Success will depend on execution in a market where OpenAI, Google, AWS, and Meta are simultaneously building their own chips and where Nvidia's ecosystem advantage remains deeply entrenched.

Nvidia's rise to become the world's most valuable company was built on repurposing GPUs originally designed for gaming into the engines of AI training and inference. That pivot created a near monopoly on the highest-end AI chips, leaving hyperscalers, startups, and now strategic investors searching for ways to diversify. OpenAI added momentum to that search in August 2026 when it announced its first AI chip, the Jalapeño, which it claimed offered industry-leading speed and efficiency. Google, AWS, and Meta are also developing their own AI processors. Euclyd is entering this crowded but capital-rich arena with a specific bet: inference workloads require a different processor and memory architecture than the GPU-centric systems that dominate training.

The startup's pitch is that its silicon systems for foundational models will reduce the energy needs and costs of AI data center infrastructure, a claim that remains unproven at scale. Euclyd is targeting two revenue streams: selling hardware and physical rack systems to enterprise customers that want secure self-hosted AI inference, and licensing its intellectual property to companies that want to develop their own chips based on pre-existing technology. CEO Bernardo Kastrup framed the broader stakes, saying that AI is becoming a foundation of economic growth, scientific discovery, and national competitiveness, but its potential will remain constrained unless infrastructure is fundamentally changed.

Samsung's involvement is more than financial. The company is one of the world's largest memory manufacturers and a major engineering force, and Kastrup emphasized that Samsung can help in more ways than money. For Samsung, backing Euclyd offers strategic access to emerging non-GPU chip architectures and possibly a closer relationship with inference-focused silicon that could drive demand for advanced memory. The co-lead structure with EQT's fund, Somerset, and Innovation Industries signals that deep tech investors see this as infrastructure-grade capital rather than a typical early-stage software bet.