Anthropic is in early discussions with Samsung Electronics to develop a custom AI chip, The Information reported on July 3, 2026. The move reflects a growing push among AI labs to secure hardware supply chains and reduce dependence on Nvidia, which dominates the AI chip market. Many technical decisions remain unresolved-whether the chip will handle training, inference, or both, and how it will slot into server architecture.
Chip shortages drive custom development
Reuters reported in April that Anthropic was exploring its own processor in response to persistent AI chip shortages. By designing an in-house accelerator, the company can tailor hardware to its models-including the Claude family of large language models-and insulate itself from supply constraints. Performance targets and the chip's exact role within the stack are still to be determined.
A growing trend in custom AI silicon
Anthropic would join a wave of companies building their own chips. Last week, OpenAI unveiled its first inference processor, Jalapeño, developed with Broadcom, which also helped Google design its TPUs. Other designs focus narrowly on specific workloads: startup Etched built an accelerator exclusively for inference, and Nvidia's LPU 30 zeroes in on a subset of inference compute. These efforts underscore how AI firms are moving deeper into hardware to match silicon to their workloads.
Samsung's manufacturing and memory strengths
Samsung offers advanced manufacturing processes that could serve a data-center chip, including a 4-nanometer node and the upcoming 2-nanometer SF2P process. SF2P uses gate-all-around transistors to limit current leakage and improve energy efficiency, with optimized interconnects that boost performance. Samsung also produces HBM (High Bandwidth Memory), the fast stacked memory used in many AI accelerators. While Anthropic hasn't confirmed using HBM, the technology would fit naturally with a high-performance chip design.
Existing partnerships remain central
Anthropic declined to confirm or deny the Samsung talks. In a statement to TechCrunch, the company said chips from Amazon Web Services, Google, and Nvidia "remain an important part of its infrastructure." A proprietary processor would complement those relationships, not replace them. In April, Anthropic committed more than $100 billion to AWS infrastructure over ten years, reinforcing its reliance on existing cloud providers.
Fits into broader data center ambitions
The potential chip aligns with Anthropic's expansion plans. Last year, it announced a $50 billion initiative with infrastructure specialist Fluidstack to build AI data centers in the United States. A custom silicon would deploy within that infrastructure, giving the company finer control over performance, energy consumption, and compute availability at a time when demand far outstrips supply.
Why this matters for IT and development
For IT and development teams managing AI deployments, the shift toward custom silicon could reshape hardware procurement, cloud cost models, and optimization strategies. A bespoke chip might offer different price-performance trade-offs than Nvidia GPUs, requiring teams to adapt tooling and workflows. With AI companies increasingly controlling the entire stack-from silicon to services-professionals who track these developments can make sharper infrastructure decisions. Resources on AI for IT & Development can help bridge the gap between emerging hardware and practical implementation skills.
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