Anthropic is reportedly exploring the development of its own AI chips to address rising demand and supply shortages. It signals a strategic shift toward greater control over AI infrastructure and performance optimization.
Anthropic is reportedly considering designing its own AI chips. The AI startup is aiming to reduce its dependence on third-party hardware providers and address growing shortages. This comes at a time when demand for high-performance AI infrastructure continues to surge globally.
According to a report by Reuters and The Information, the company is evaluating the feasibility of developing in-house chips, although the effort is still in its early stages with no final design or dedicated engineering team in place yet.
At present, Anthropic relies on a mix of hardware from big players like Nvidia, Google and Broadcom. It also uses cloud-based chips such as Amazon’s Trainium and Inferentia processors, along with Google’s tensor processing units (TPUs) for training and running its AI models, including the Claude chatbot. However, with AI adoption, access to such resources is getting difficult.
Anthropic has already partnered with Google and Broadcom to co-develop the TPU infrastructure to expand its AI computing capacity in the United States. While the company is expanding its strategic partnerships, this new in-house chip development shows the pressure on AI firms to secure dedicated resources.
Demand for its AI model Claude has accelerated in 2026, with the startup's run-rate revenue now surpassing $30 billion, up from about $9 billion at the end of 2025, Anthropic said earlier this week. Designing an advanced AI chip can cost roughly half a billion dollars, according to industry sources, as companies need to employ skilled engineers and spend money to make sure the manufacturing process has no defects.
As per reports, industry estimates suggest that development costs can exceed $500 million. Not only that, but the company will also need specialized talent and long timelines for design, testing and large-scale manufacturing. But for now, these ambitions remain exploratory.
For established chipmakers like Nvidia, this can be a gradual shift as customers look to reduce reliance on external suppliers.