Anthropic is hiring engineers to design custom AI chips as demand for its Claude AI model grows and the company seeks to reduce reliance on external hardware suppliers.
The move mirrors similar strategies by OpenAI, Google DeepMind, and Meta, which have all developed proprietary chips or accelerators to run their AI models more efficiently and scale faster.
What happened
Anthropic is assembling a team to design its own custom chips for AI usage, planning to co-design hardware and models to help its technology run faster and more efficiently. The company confirmed this with TechCrunch after Business Insider first reported the news.
Why it matters
Demand for Claude is rising while Anthropic relies on hardware deals with AWS, Google, Nvidia, and AMD. Building custom chips, similar to moves by OpenAI (which unveiled its Broadcom-built Jalapeño chip for inference), allows Anthropic to scale independently rather than depending entirely on external suppliers.
What to watch
The company is recruiting engineers with chip design experience for its custom silicon team. Last month, The Information reported Anthropic was scouting Samsung as a potential partner for building such chips.
Anthropic is building a team to design custom chips optimized for AI usage, the company confirmed with TechCrunch after Business Insider first reported the news. The company plans to co-design hardware and models to help its technology run faster and more efficiently. Last month, The Information reported that Anthropic was scouting Samsung as a potential partner for building such chips.
Anthropic’s decision to build its own silicon comes as demand for Claude grows while the company continues to rely on existing deals with AWS, Google, Nvidia, and AMD for AI computing hardware. However, as the company scales, relying on external suppliers alone is insufficient to meet demand. The company is now seeking engineers with experience in chip design for its "custom silicon team," according to a job listing.
Anthropic is not alone in this strategy. In June, OpenAI unveiled its Broadcom-built Jalapeño chip, which is designed specifically for inference workloads. Google DeepMind has long relied on Alphabet's TPU chips to power its AI models, while Meta has been developing its own MTIA accelerators for AI workloads. The trend reflects a broader industry recognition that custom silicon provides a path to faster performance and greater operational efficiency when running large AI models.
Anthropic's move to build a custom chip team reflects a broader industry trend in which AI leaders are taking vertical control of their infrastructure. The company's existing relationships with AWS, Google, Nvidia, and AMD provide access to compute, but these partnerships have limits when demand outpaces supply. By co-designing hardware tailored to Claude's architecture, Anthropic mirrors strategies already deployed by OpenAI, Google DeepMind, and Meta—each recognizing that proprietary silicon can deliver competitive advantages in both performance and cost efficiency.
The timing is significant. Anthropic is reportedly scouting Samsung as a manufacturing partner, suggesting the company is moving beyond the design phase toward actual production capability. This vertical integration allows Anthropic to iterate on hardware and models together, a model that has proven valuable for other AI leaders who depend on custom silicon to handle their largest inference workloads.
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