The massive financial cost of training and running LLMs is sparking a quiet rebellion among top-tier artificial intelligence laboratories. Eager to break free from their near-total reliance on Nvidia’s dominant graphic processing units, major AI firms are designing their own specialized components. According to The Information, Anthropic has officially entered early-stage developmental talks with Samsung Electronics to manufacture its first custom-built AI chip.
The 2nm strategy
The engineering project remains in a nascent phase. In other words, Anthropic has not yet locked down the chip’s exact processing limitations, system architecture, or target server configurations. However, corporate leaks reveal that the AI developer is explicitly evaluating Samsung’s upcoming 2nm manufacturing process. It’s also seeking the Korean giant’s advanced semiconductor packaging facilities.
To turn these blueprints into actual hardware, Anthropic recently expanded its internal infrastructure group. The AI giant hired Clive Chan—a prominent hardware engineer who previously worked on OpenAI‘s dedicated custom silicon initiatives.
If finalized, a manufacturing contract would be a huge milestone for Samsung’s contract foundry ecosystem. The tech conglomerate has actively poured capital into its hardware divisions. Samsung recently partnered with SK Group to orchestrate a staggering $518 billion investment over the next decade to establish four massive semiconductor installations in South Korea. Sourcing an exclusive client like Anthropic would provide Samsung with the perfect showcase piece to challenge TSMC. Currently, TSMC enjoys a comfortable near monopoly on high-end chip fabrication.
Following a familiar industry playbook
Anthropic’s latest technological exploration closely mirrors strategies deployed by its fiercest industry rivals. Just last month, OpenAI teamed up with hardware design group Broadcom to unveil its inaugural, highly efficient custom inference processor, code-named “Jalapeño.” Similarly, technology gatekeepers like Google, Amazon, Meta, and Microsoft have spent years integrating customized Tensor Processing Units (TPUs) into their local cloud server ecosystems. In-house AI hardware allows maximizing efficiency and driving down baseline operational overhead.
When questioned about the potential hardware partnership, an Anthropic representative noted that diversifying processing pathways remains central to the organization’s overarching scaling roadmap. The company emphasized that going forward, its computing footprint will remain centered on tapping into a multi-vendor environment. This includes including Amazon Web Services’ Trainium platforms, Google’s specialized TPUs, and Nvidia’s dominant GPUs.
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