Anthropic has opened early-stage discussions with Samsung Electronics to manufacture a custom AI accelerator on Samsung's upcoming 2-nanometer foundry process, according to a July 2 report that jolted the semiconductor and AI communities. The talks signal a deepening industry shift toward vertically integrated hardware, with AI developers increasingly bypassing off-the-shelf GPUs from Nvidia to squeeze out better performance, lower latency, and tighter cost control. For Anthropic, the maker of the Claude large language model, a homegrown chip would mark its most aggressive hardware move yet—mirroring similar custom silicon efforts at Google, Amazon, Microsoft, and reportedly OpenAI.
Samsung’s 2nm node, scheduled for risk production later this year and mass production in 2025, relies on gate-all-around (GAA) transistor technology to deliver up to a 25% power reduction, 12% performance gain, and 5% area scaling over its 3nm process. Winning a high-profile AI customer like Anthropic would hand Samsung Foundry a crucial endorsement as it battles TSMC for dominance in the sub-3nm era. TSMC currently fabricates Nvidia’s H100 and upcoming Blackwell GPUs, which power the vast majority of AI training runs. An Anthropic deal would prove that Samsung can deliver cutting-edge logic chips for the most demanding workloads—something it has struggled to demonstrate at scale since its 5nm node.
The report, attributed to people familiar with the matter but not authorized to speak publicly, cautions that negotiations remain “exploratory” and no binding agreement exists. Anthropic and Samsung declined to comment. Still, the disclosure underscores how swiftly the AI accelerator landscape is evolving. In 2023 alone, global spending on AI chips exceeded $50 billion, with Nvidia holding more than 80% of the market. That concentration has driven hyperscalers and AI labs to fund their own silicon, both to sidestep Nvidia’s multi-month lead times and to tailor architectures precisely to their models.
For Anthropic, the calculus is straightforward. Claude 3.5 Sonnet and the forthcoming Opus iteration require enormous compute for both training and inference. Running inference on general-purpose GPUs leaves performance on the table; a custom accelerator optimized for transformer-based architectures can slash token-generation latency and power consumption. Anthropic already partners with Google Cloud, which offers TPUs, but owning the whole stack would let the company fine-tune hardware to its Constitutional AI training framework and long-context attention mechanisms—capabilities that distinguish Claude from GPT-4o.
Moreover, a bespoke chip could reduce Claude’s per-query cost, making the service more competitive as enterprise adoption accelerates. Microsoft, which has invested billions in OpenAI, incorporates Copilot and GPT models throughout Windows 11 and its productivity suite, yet also offers Claude models via Azure. If Anthropic can field a cost-efficient inference chip, it could pressure Microsoft’s own Maia accelerator timeline while offering Windows developers an alternative high-performance AI backend.
Samsung’s 2nm process, formally SF2, introduces a third-generation GAA architecture with nanosheets that provide superior electrostatic control compared to FinFET. The company has disclosed tape-outs for mobile and high-performance computing applications and plans to start mass production at its Hwaseong campus by mid-2025. However, Samsung has previously stumbled on yield ramps—its 3nm GAA node has seen limited external customer uptake—so landing a marquee partner like Anthropic would be a make-or-break validation. If yields falter, Anthropic could pivot to TSMC’s upcoming N2 node, though that process is also in its infancy and heavily booked by Apple and Nvidia.
The semiconductor supply chain adds further complexity. Custom AI accelerators demand advanced packaging technologies such as chip-on-wafer-on-substrate (CoWoS) or Samsung’s I-Cube to stack high-bandwidth memory (HBM) alongside logic. Samsung has invested heavily in both HBM3e memory and its advanced packaging facility in Cheonan, but it trails TSMC’s mature CoWoS-L and CoWoS-S capacity. Any Anthropic chip would likely require multiple reticle-sized dies stitched together, a technique known as multi-die integration, which remains challenging on GAA nodes.
From a strategic perspective, the talks illustrate how the AI stack is being re-architected from the ground up. Anthropic’s move follows OpenAI’s reported discussions with TSMC and Broadcom to assemble its own accelerator. Google’s TPU v5p already powers internal training, and Amazon’s Trainium2 is entering production. Microsoft’s Maia 100 accelerator, purpose-built for Azure AI workloads, underscores the trend of cloud providers hedging against Nvidia’s pricing power. Nvidia has responded by accelerating its own road map—announcing a one-year cadence for new GPU architectures—and by bundling its GPUs with proprietary networking and CUDA software to raise switching costs.
For Windows users, the development matters because it influences the cost, speed, and availability of AI features integrated into the operating system. Microsoft’s Copilot+ PCs lean heavily on Qualcomm’s Snapdragon X neural processing units, but cloud-based AI services still rely on Nvidia GPUs inside Azure. If Anthropic’s custom chip makes Claude inference radically cheaper, Microsoft could choose to deepen its Claude integration or face pricing pressure on its own models. Third-party developers building AI-powered applications on Windows would also benefit from a more diverse and price-competitive accelerator market.
Anthropic’s engineering muscle, which includes veterans from Google’s TPU team, suggests the company has the talent to design a competitive accelerator. Yet the undertaking remains immense: a full-custom chip on a cutting-edge node can cost upwards of $500 million in design, mask, and verification work, and two to three years to bring to volume production. Anthropic, valued at $18.4 billion after a $750 million injection from Menlo Ventures in May 2024, has the capital, but the execution risk is high.
Industry analysts caution that Samsung has a shaky track record with advanced-node execution. Its 4nm node suffered from poor yields, costing it major contracts with Qualcomm and Nvidia. The 2nm node, built on GAA, is a technological leap that has yet to prove itself in high-volume manufacturing for large, complex dies. Conversely, Samsung’s willingness to offer competitive pricing and dedicated capacity could be exactly what Anthropic needs to get a chip into production faster than it could through TSMC’s fully-booked pipeline.
The timeline remains speculative. Even if the two companies sign a contract this year, chip design, tape-out, validation, and software enablement would likely push volume shipments of a usable accelerator into 2027 or later. During that period, Nvidia will have shipped at least two new GPU generations, and the AI model landscape will have evolved. Anthropic would need to bake in enough flexibility to handle future model architectures, including mixture-of-experts designs and ever-larger context windows.
In the near term, the talks themselves send a powerful signal. They demonstrate that the post-GPT moment is one where AI labs no longer see themselves purely as software shops but as hardware-defining organizations. They also validate Samsung’s bet on GAA technology at a time when investors question its ability to compete with TSMC. For Windows enthusiasts tracking the hardware that will power the next wave of AI, the Anthropic-Samsung thread is one to monitor closely—it could determine whether the next breakthrough model runs on a Samsung-fabricated chip inside an Anthropic datacenter, challenging the established Nvidia-TSMC duopoly.
The bigger picture is a semiconductor industry in flux. Geopolitics, reshoring of chip manufacturing, and the insatiable appetite for AI compute are reshaping alliances. Samsung’s talks with Anthropic hint at a future where AI chip demand is so vast that even dominant foundries cannot serve every customer, opening doors for ambitious challengers. As one semiconductor executive quipped, “In the AI era, every large model builder eventually becomes a chip designer.” Anthropic appears poised to prove that adage true.