Anthropic Eyes Samsung Partnership to Build Next-Gen AI Chips

Anthropic has entered preliminary talks with Samsung to manufacture a custom AI chip using Samsung’s advanced 2nm process, a move that could reshape the AI hardware landscape by reducing dependence on Nvidia and optimizing costs for running Claude at scale. While no design or production timeline has been finalized, the discussions highlight Anthropic’s ambition to build in-house silicon expertise and Samsung’s push to challenge TSMC in cutting-edge foundry services. 

Anthropic’s Strategic Shift

Anthropic, valued at nearly $965 billion, is exploring custom silicon as part of a broader industry trend where leading AI labs seek control over their hardware supply chains.
The company has begun defining specifications for a processor that could handle inference workloads—the computationally intensive task of running large language models like Claude—rather than training, which remains dominated by Nvidia GPUs and Google TPUs. This mirrors OpenAI’s unveiling of its Broadcom-designed “Jalapeño” inference accelerator.

The hiring of Clive Chan, a key engineer from OpenAI’s chip team, underscores Anthropic’s intent.

Chan helped design Jalapeño and brings expertise in building accelerators from the software layer up, giving Anthropic a strong foundation for its own silicon program.

Why Samsung?

Samsung’s appeal lies in both financial alignment and manufacturing capability.
The company participated in Anthropic’s $65 billion Series H round in May 2026, alongside SK Hynix and Micron, but unlike those firms, Samsung operates a foundry capable of producing advanced logic chips.

The talks focus on Samsung’s SF2 2nm process, which uses Gate-All-Around (GAA) nanosheet transistors for improved efficiency and density, and its advanced packaging technology, critical for high-bandwidth connections between logic and memory.

If successful, Anthropic would become a marquee client for Samsung Foundry, bolstering its bid to compete with TSMC, the current leader in advanced semiconductor manufacturing.

Industry Context

The move reflects a broader diversification trend in AI hardware.

Nvidia currently controls about 74% of the AI chip market, but soaring GPU costs—H100 units sell for over $30,000 each—and supply constraints are driving AI labs to explore alternatives.

Custom inference chips could reduce Anthropic’s costs by 30–50%, while ensuring more predictable access to compute capacity.

• OpenAI partnered with Broadcom for Jalapeño.
• Google continues to scale its in-house TPUs.
• Amazon offers Trainium and Inferentia chips via AWS.
• Meta is exploring custom silicon but remains in early stages.

Risks and Challenges

Despite the promise, the project remains nascent.

Anthropic has not finalized specifications, prototypes, or a manufacturing timeline.
Consulting multiple chip design firms suggests it may outsource parts of the architecture.
Even if development proceeds, production-ready chips could be years away, leaving Anthropic reliant on Nvidia, Google, and Amazon in the near term.

Implications

If Anthropic and Samsung succeed, the partnership could mark a turning point in AI infrastructure.

For Anthropic, it means greater autonomy, lower costs, and tailored performance for Claude.

For Samsung, it offers a chance to secure a high-profile AI client and strengthen its position against TSMC.

For the industry, it signals a future where AI labs own their full stack—from silicon to software—reducing reliance on external suppliers.