Anthropic has begun developing its own AI chips in collaboration with Samsung, aiming to reduce reliance on Nvidia’s scarce accelerators amid growing computational demands for large AI models, according to sources cited by The Information. The project’s goals and expected performance remain undisclosed, but this marks a strategic move for Anthropic to gain more control over its infrastructure in an era when computing capacity is a major bottleneck for AI development.
This initiative didn’t come out of nowhere. Earlier this year, Anthropic discussed building custom chips to shield itself from supply chain disruptions. Now, the project has reportedly entered an active development phase. While the company hasn’t formally confirmed the Samsung partnership, Anthropic told TechCrunch it will continue relying on a ”diversified hardware stack” that includes offerings from Google, Amazon, and Nvidia.
Details about the chips remain scarce. It’s unclear whether Anthropic is designing an accelerator optimized for training large models, inference chips focused on production deployments, or highly specialized solutions for niche workloads. The distinction is critical: training chips prioritize memory bandwidth and throughput, whereas inference hardware in 2026 is increasingly the cost-saving battleground since inference dominates ongoing AI expenses after deployment.
Choosing Samsung as a manufacturing partner is a logical step. South Korea’s Samsung is the world’s second-largest contract chip foundry after TSMC, and it’s aggressively expanding its AI semiconductor portfolio-not just in advanced process nodes but also in advanced packaging technologies like CoWoS and high-bandwidth memory (HBM). For Anthropic, this partnership offers an alternative to TSMC, which currently faces a long queue of AI chipmakers vying for production slots.
Anthropic and Samsung’s AI chip development partnership
The move toward custom AI silicon is no longer unusual. Google has been developing its TPUs since 2016, deploying them internally and through cloud services. Amazon builds its own Inferentia and Trainium chips to reduce AWS’s Nvidia dependence and protect cloud margins. Meta develops its MTIA chips for recommendation systems and generative AI. Against this backdrop, Anthropic appears more as a late entrant catching up than a pioneer blazing a new trail.
Competitive pressure has intensified recently. OpenAI, Anthropic’s main rival, unveiled its own inference chip called Jalapeño, co-developed with Broadcom. Early tests reportedly show Jalapeño is more energy-efficient than general-purpose AI GPUs. When one company hints at cutting inference costs, others usually respond not with announcements but by ramping up chip orders, hiring hardware engineers, and negotiating factory access.
Anthropic’s hardware strategy also reflects its cloud partnerships. Amazon and Google both provide Anthropic with computing resources while serving as its major investors. This arrangement supports growth but limits Anthropic’s control over cost structures and hardware specialization. Owning a custom chip-even if initially for a narrow set of tasks-would give Anthropic leverage in negotiations and the flexibility to fine-tune its Claude models for specific hardware.
Samsung benefits too. Besides working with Nvidia and discussing chip production with Google, Samsung recently secured a contract from Tesla for next-gen AI accelerators. For Samsung’s foundry business, hosting Anthropic’s project is not just about revenue but about positioning itself as a primary choice for companies designing proprietary AI chips-not just a fallback to TSMC.
The broader industry context highlights why this matters. Analysts estimate TSMC still commands the lion’s share of high-end AI chip manufacturing, while demand for sophisticated packaging like CoWoS and HBM remains tight. For developers of large models, performance is vital, but guaranteed production capacity is increasingly a strategic asset. Proprietary chip design doesn’t solve supply shortages outright, but it helps reserve production slots and enables long-term infrastructure planning instead of quarterly scramble.
- Google has been developing TPUs since 2016.
- Amazon produces Inferentia and Trainium chips for AWS.
- OpenAI makes inference chips in partnership with Broadcom.
- Samsung is expanding its AI semiconductor order book.
Anthropic’s next steps hinge on what type of chip it is building. Inference accelerators could materialize sooner as their scope is easier to define and they scale with cloud deployment. Training chips, on the other hand, require longer development cycles and impose tougher demands on memory capacity, interconnects, and packaging.
The key question won’t be answered until 2027 or later-when Anthropic’s chip can be compared with OpenAI’s Jalapeño and Google and Amazon’s cloud silicon on cost per token processed and power efficiency. These performance and economic metrics, not just the fact of producing silicon, will determine whether Anthropic’s move truly cuts operating costs or becomes just another costly R&D venture.
Note: Meta, mentioned in this article, is designated as an extremist organization and banned in Russia.