Where the Talks Stand
The discussions are described as exploratory. According to The Information, Anthropic has not yet determined what the chip will do, how powerful it will need to be, or how it would integrate into a server architecture. The conversations are focused on understanding Samsung’s manufacturing capabilities rather than defining a finished product.
Anthropic is evaluating Samsung’s 2-nanometer SF2P manufacturing process and its advanced packaging facilities, both relevant to producing a high-performance custom inference accelerator. Samsung’s in-house High Bandwidth Memory (HBM) production is an additional factor — custom AI chips require large amounts of fast memory tightly coupled to the processor, and Samsung is one of the few foundries that produces both the logic chip and the memory in-house.
The Hiring Signal
Anthropic’s chip ambitions have been signalled by its recent hiring activity. In early June 2026, the company hired Clive Chan, who had previously spent two and a half years at OpenAI building its Broadcom-designed custom inference accelerator — a chip internally known as “Jalapeño.” Chan was reportedly the second engineer ever to join OpenAI’s dedicated custom chip team, making him one of the most experienced custom AI silicon specialists in the industry. His arrival at Anthropic suggested that the company’s chip plans were moving beyond early brainstorming.
Why Custom Silicon Matters
Nvidia’s H100 and H200 GPUs currently dominate the market for training and running frontier AI models, and the company commands enormous pricing power as a result. For AI labs like Anthropic that are spending hundreds of millions of dollars annually on compute, even modest efficiency gains from a custom chip purpose-built for inference workloads could translate into significant cost reductions at scale.
OpenAI has already demonstrated the strategic value of this approach. Its “Jalapeño” inference chip, developed with Broadcom and manufactured by TSMC, reportedly delivers better performance-per-watt than competing options. With OpenAI having moved first, Anthropic’s conversations with Samsung may be as much about competitive necessity as cost optimisation.
Why Samsung and Not TSMC?
TSMC — the world’s dominant chip foundry — is the obvious first choice for any new custom silicon project, and it manufactures chips for Apple, Nvidia, AMD, and most major chip designers. However, TSMC’s capacity is heavily committed, and lead times for advanced-node production can stretch considerably. Samsung’s foundry business has been investing aggressively in its 2nm and 3nm process technology, and the company is actively seeking high-profile customers to establish its advanced nodes as credible alternatives to TSMC. An Anthropic partnership would be a significant win for Samsung Foundry’s business development efforts.
Industry Context: The Custom Chip Race
Anthropic is not alone in pursuing dedicated silicon. Google has been running its own Tensor Processing Units (TPUs) for nearly a decade. Amazon Web Services offers its own Trainium and Inferentia chips. Microsoft has developed its Maia AI accelerator. Meta has developed its Training and Inference Accelerator (MTIA). The convergence on custom silicon across the industry reflects a shared conclusion: that general-purpose GPUs, however capable, are not the optimal or most cost-effective solution for every AI workload at scale.
For Anthropic, a company that has staked its existence on building reliably safe and increasingly capable AI, controlling more of its hardware stack could also provide meaningful strategic independence from Nvidia’s supply chain — an asset whose importance becomes clearer with every quarter that GPU demand outstrips supply.
What Comes Next
Given the early stage of the discussions, a finished Anthropic chip is likely years away at minimum. Custom silicon projects typically require two to four years from initial design to commercial deployment, and Anthropic is still in the specification phase. In the near term, the company will continue to rely on Nvidia GPUs and cloud compute partnerships. But the direction is clear: like its rivals, Anthropic is building toward a future where it owns more of the infrastructure that powers its models.
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