Anthropic Builds Custom AI Chips for Claude: Inside the Major Shift Toward In-House Silicon

Anthropic has not disclosed a target date for tape-out or production, which is normal for a program at this stage .

How Other Major AI Companies Are Building Custom Chips

OpenAI and the Jalapeño Chip

OpenAI unveiled its first custom AI chip, codenamed Jalapeño, on June 24, 2026 . Designed in collaboration with Broadcom and manufactured by TSMC, Jalapeño is an inference-specialized processor . OpenAI began the design effort in early 2025 and initially explored building its own foundries before pivoting to a design-partner model . The company has also announced a broader strategic collaboration with Broadcom worth up to $10 billion to deploy accelerator and networking systems through 2029 .

Google DeepMind and Eight Generations of TPUs

Google has the longest-running in-house AI chip program, with over a decade of custom silicon development. Its Tensor Processing Unit (TPU) family now spans eight generations .

The Ironwood TPU (7th gen), launched in late 2025, can connect 9,216 chips in a single pod for training and inference . In April 2026, Google announced its 8th-generation TPUs with two purpose-built architectures: TPU 8t for training and TPU 8i for inference . Google has also reportedly tapped AMD to help design one of its 10th-generation TPUs .

Notably, Google also supplies TPUs to Anthropic under a massive deal worth tens of billions of dollars, providing up to one million chips .

Meta and the MTIA Family

In March 2026, Meta unveiled a roadmap of four new in-house chips under its Meta Training and Inference Accelerator (MTIA) family . The MTIA 300 was already deployed; the MTIA 400, MTIA 450, and MTIA 500 are scheduled for release roughly every six months through 2027 .

Meta is co-developing these chips with Broadcom and plans to start manufacturing one of them in September 2026 as part of a push to double its overall computing capacity . The MTIA family covers both inference and, for the first time, custom training chips — previously Meta relied on Nvidia GPUs for model training .

Summary of Custom AI Chip Programs

What This Means for the AI Hardware Landscape

The shift toward custom silicon represents a fundamental change in how AI companies approach infrastructure. Rather than competing solely on model architecture and training data, the industry's leaders are now building hardware as a competitive advantage.

For Anthropic, the custom chip program is still in its earliest stages — no target date for production has been set, and no architectural details or manufacturing agreements have been announced . But the move signals that Anthropic views chip design as essential to its long-term strategy, especially as inference costs grow with the scale of deployments.

For the industry as a whole, the diversification away from Nvidia's GPUs could have far-reaching implications. If custom chips deliver on their promise of lower costs and higher performance, the economics of AI inference could shift dramatically, making AI applications more affordable and accessible.