Anthropic Explores Custom AI Chip Partnership with Samsung to Reduce Nvidia Dependence

Posted on 30th Jul 2026 06:03:16 in Artificial Intelligence, Machine Learning

Tagged as: Anthropic, Samsung, AI chip, custom silicon, semiconductor, Nvidia, artificial intelligence, 2nm processor

Anthropic, the artificial intelligence company behind the Claude family of large language models, has initiated early-stage work on a custom AI chip and entered discussions with Samsung Electronics as a potential manufacturing partner, according to a report from The Information published on July 2, 2026. The move signals a major strategic step for the company as it seeks to reduce its reliance on Nvidia's dominant GPU infrastructure and join a growing list of AI firms developing proprietary silicon.

The project is still in its formative stages, with no detailed design or manufacturing work yet underway. Sources familiar with the matter told The Information that Anthropic is specifically evaluating Samsung's advanced 2-nanometer manufacturing process and the Korean conglomerate's cutting-edge chip packaging facilities. However, the company may ultimately decide not to proceed with the project, underscoring the early and exploratory nature of the discussions.

Strategic Context: The Race for Custom AI Silicon

Anthropic's chip ambitions come at a critical juncture in the AI industry, where access to computing power has become the single most important factor determining a company's ability to train and deploy advanced models. The news follows closely on the heels of OpenAI's unveiling of its first custom inference processor, dubbed Jalapeño, developed in partnership with Broadcom and announced on June 24, 2026. OpenAI's chip was specifically designed to handle the inference workload, the process of running pre-built AI models in response to user queries, and reportedly delivers significantly better performance-per-watt than current alternatives.

The trend toward homegrown silicon extends well beyond just OpenAI and Anthropic. Google has long offered custom Tensor Processing Units through its cloud platform. Amazon Web Services provides Trainium and Inferentia chips for machine learning workloads. Microsoft has developed the Maia 200 AI accelerator for Azure, built on TSMC's 3nm process with over 140 billion transistors. Meta has also invested heavily in custom chip design. Even Apple is reported to be developing separate chips specifically for AI server workloads, according to Bloomberg.

For Anthropic, the strategic rationale is clear. Nvidia currently commands an estimated 74 percent of the AI chip market, according to The Information's analysis. By developing its own silicon, Anthropic gains leverage in procurement negotiations and reduces its vulnerability to GPU shortages and pricing pressures that have plagued the entire AI industry. As Stacy Rasgon, analyst at Bernstein, told Axios: "I want something in my pocket when I'm sitting across the table from Jensen negotiating," referring to Nvidia CEO Jensen Huang.

Anthropic's Engineering Buildout and Partnership Strategy

A key signal of Anthropic's seriousness about its chip program is its recent hiring of Clive Chan, an early member of OpenAI's own custom chip team, according to The Information. The addition of Chan underscores Anthropic's deliberate engineering buildout and its commitment to building in-house semiconductor expertise.

Anthropic has also been in discussions to use chips from Microsoft and from UK-based startup Fractile, according to sources, reinforcing a deliberate multi-vendor approach rather than a wholesale pivot away from established suppliers. This diversified hardware strategy aligns with the company's public stance: when reached for comment, Anthropic told TechCrunch that Amazon Web Services's Trainium chips, Google tensor processing units, and Nvidia graphics processors "will remain central to how the company scales its compute strategy."

The choice of Samsung as a potential manufacturing partner carries its own strategic significance. Samsung is already deeply embedded in the AI chip ecosystem, serving as a major manufacturing partner for Nvidia itself. The two companies are jointly building an AI chip factory in South Korea. Samsung has also discussed partnering with Google on its chip-making efforts. Importantly, Samsung, along with SK Hynix and Micron, participated in Anthropic's massive $65 billion fundraising round in May 2026, creating a pre-existing financial relationship between the companies.

For Samsung's foundry business, winning a marquee AI client like Anthropic would be a significant competitive victory against Taiwan Semiconductor Manufacturing Company (TSMC), which currently dominates the market for cutting-edge AI chip manufacturing. Samsung is reportedly positioning its 2nm process as a viable alternative to TSMC's N2 node, though analysts have repeatedly raised concerns about Samsung's historical struggles with advanced process yields relative to TSMC.

The stakes for Samsung are enormous. Earlier in the same week, Samsung Group and SK Group announced a decade-long combined investment of $518 billion to build four memory-chip plants in South Korea. This commitment underscores the scale of capital being deployed across the Korean chip ecosystem as it competes for AI infrastructure mandates against TSMC's established dominance.

Industry Implications: A Crowded but Constrained Supply Chain

The rush to develop custom AI chips reflects a widespread industry conviction that demand for AI computing will continue rising at an extraordinary pace. Nvidia has suggested it would do $1 trillion in cumulative revenue from 2025 through 2027 alone. Even a small fraction of that market captured by competitors could represent tens of billions of dollars in revenue.

However, the path to custom silicon is fraught with challenges. While designing chips has become easier thanks to AI-assisted tools, turning a design into a physical product requires access to a semiconductor supply chain that is already stretched to its limits. TSMC handles the majority of cutting-edge chip manufacturing globally, and despite investing tens of billions of dollars to expand capacity, executives have repeatedly stated that demand continues to outstrip supply.

The problem extends beyond just fabrication capacity. Companies developing custom chips also compete for advanced packaging capacity, high-bandwidth memory, and lithography equipment from ASML, the Dutch company that holds a near-monopoly on the most advanced chipmaking tools. "If you're just starting to design a chip right now, you won't see silicon for three years," Rasgon warned.

Even companies that successfully develop custom chips continue buying Nvidia GPUs for many workloads. Nvidia's hardware, networking infrastructure, and software ecosystem, particularly its CUDA platform, remain extraordinarily difficult to replicate. As the Axios analysis noted, the total cost of ownership can still be lower when using Nvidia chips given how powerful and optimized they are for AI workloads.

China's DeepSeek is the latest company reportedly working on its own AI chips, per Reuters, adding another dimension to the global race for semiconductor self-sufficiency. The move highlights how AI chip development has become a matter of both corporate strategy and geopolitical importance.

What This Means for the Future of AI Infrastructure

The broader significance of Anthropic's chip discussions with Samsung extends well beyond any single partnership. The AI industry is undergoing a fundamental structural shift from relying on a single dominant hardware supplier toward a more diversified ecosystem where leading AI labs own at least some of their compute infrastructure.

This shift mirrors patterns seen in earlier technology transitions. As the mainframe gave way to the personal computer, and as proprietary Unix systems yielded to Linux and open-source software, each transition was driven by a desire for greater control, lower costs, and the ability to optimize across the full technology stack. The AI chip transition follows the same logic: companies that control their hardware can optimize every layer from silicon to software for their specific workloads.

For Anthropic specifically, a successful custom chip program could provide meaningful cost advantages in running inference workloads at scale. Given that inference costs represent a growing share of AI companies' operational expenses, even modest efficiency gains translate into substantial savings. OpenAI emphasized this point when unveiling its Jalapeño chip, noting that "because OpenAI operates across the stack, each layer can be optimized around the same goal: making its models faster, more reliable, and more affordable for users."

What remains to be seen is whether Samsung can convert these early conversations into confirmed production agreements. Any confirmed foundry deal with Anthropic would be a material positive for Samsung's foundry revenue outlook and would increase competition with TSMC's near-monopoly on leading-edge AI chip manufacturing. Conversely, if Samsung's 2nm yields disappoint as they have at some earlier nodes, TSMC's competitive advantage will only widen further.

Sources

• TechCrunch — Anthropic is discussing a new custom chip with Samsung
• Investing.com/Yahoo Finance — Anthropic explores Samsung 2nm chip partnership
• Axios — The AI chip rush is crowding the same narrow pipeline
• TechCrunch — OpenAI unveils its first custom chip, built by Broadcom
• The Information — Anthropic in Talks With Samsung to Manufacture Custom AI Chip