Anthropic is reportedly exploring the development of a custom AI chip and has held discussions with Samsung over a potential collaboration, according to The Information. The move places the AI startup among a growing number of technology firms seeking greater control over hardware as demand for AI computing surges.
Earlier reports from Reuters indicated that Anthropic was considering in-house chip design to address concerns about AI hardware availability and supply constraints. However, key details of the proposed processor—including its intended use, integration into server systems, and performance targets—remain unconfirmed.
According to the report, Anthropic has not finalized the chip’s specifications and declined to comment on the discussions with Samsung. The company reiterated that its current infrastructure strategy continues to rely on chips from Google, Amazon, and NVIDIA, and that there are no further updates on potential collaboration talks with Samsung at this time.
The reported move reflects a wider trend among AI companies investing in custom silicon to improve efficiency and reduce dependence on dominant chip suppliers—particularly NVIDIA, which leads the market for AI accelerators used in large-scale model training and inference. As demand for AI computing expands across industries, firms are looking for greater control over cost, performance, and supply chains.
Anthropic’s discussions come shortly after other major technology firms advanced their custom chip efforts. OpenAI recently introduced its first custom inference chip, Jalapeno, developed with Broadcom. Amazon continues to scale its Trainium chips for AI training workloads, and Google is expanding access to its Tensor Processing Units (TPUs) via its cloud infrastructure.
Samsung already plays a pivotal role in the global semiconductor supply chain, manufacturing advanced chips for companies like NVIDIA and exploring partnerships with others such as Google. The talks between Anthropic and Samsung underscore the increasing convergence of AI software development and semiconductor manufacturing, as firms pursue tighter integration between hardware and AI systems.
