Anthropic in talks with Samsung on custom AI chip, escalating hardware independence race

Anthropic Explores Custom Chip Partnership with Samsung

Anthropic is advancing its hardware strategy by engaging Samsung in discussions about developing a proprietary AI chip, according to reporting from The Information cited by TechCrunch. The collaboration remains in exploratory phases, with fundamental design questions unresolved—the companies have not settled on the chip’s target applications, system architecture, or computational capacity.

This initiative builds on a pattern Anthropic established in April, when Reuters first reported the company was investigating custom silicon as a hedge against supply chain bottlenecks. The Samsung discussions represent a concrete step toward that goal, signaling Anthropic’s intent to diversify its compute infrastructure beyond Nvidia’s dominant position.

The Broader Hardware Independence Movement

The custom-chip trend has accelerated across the AI industry as large language model developers seek to escape Nvidia’s supply constraints and optimize silicon for their specific inference and training workloads. OpenAI announced last week a collaboration with Broadcom on “Jalapeño,” an inference processor the company claims delivers superior performance-per-watt efficiency compared to alternative accelerators. Google and Amazon have deployed their own Tensor Processing Units (TPUs) as foundational elements of their cloud infrastructure for years.

Samsung itself occupies a strategic position in this ecosystem. The conglomerate manufactures components for Nvidia’s training processors while relying on Nvidia’s software stack for chip design. Separately, Samsung has explored chip-manufacturing partnerships with Google, positioning itself as a potential manufacturing partner for custom silicon across multiple customers.

Anthropic’s Measured Response

When asked to comment by TechCrunch, Anthropic declined to confirm specifics about the Samsung partnership, stating only that a diversified hardware portfolio incorporating processors from Google, Amazon, and Nvidia remains central to its compute strategy. The restraint suggests either confidentiality agreements or genuine uncertainty about whether the partnership will advance beyond preliminary discussions.

Why This Matters

For teams evaluating Anthropic’s long-term cost structure and inference latency—particularly those considering multi-year commitments to Claude—custom silicon development signals potential future pricing efficiency gains and reduced dependency on Nvidia’s H100 and H200 GPU availability. However, the early-stage nature of these talks means any custom Anthropic-optimized accelerator is unlikely to reach production deployment before 2027 at earliest. In the near term, Anthropic’s inference economics remain tethered to spot pricing for Nvidia hardware and competition for cloud-provider quotas. The Samsung discussions represent a strategic hedge rather than an imminent shift in the underlying cost structure.