The race among leading AI model companies to develop their own custom chips is intensifying. For OpenAI and Anthropic, this initiative is about more than just reducing computing costs; it's a strategic move to secure supply, optimize model architecture, and gain a stronger position in the upcoming competition for AI infrastructure dominance.
According to a recent report, Anthropic is in discussions with Samsung regarding custom AI chips and has commenced early-stage development of its own proprietary AI processors. If these custom server chips reach mass production, it would mark a significant step toward hardware independence for the company behind the Claude AI model.
This move is seen as Anthropic following in the footsteps of OpenAI, which has been advancing its own custom AI chip project for a longer period. OpenAI is collaborating with chip design and manufacturing partners to build a more independent and efficient computing infrastructure for products like ChatGPT. Together, these companies signal a broader trend: major AI model firms are shifting from pure algorithmic competition to an integrated hardware-software approach.
The market impact is expected across several fronts: the bargaining power of external GPU suppliers like Nvidia, opportunities for foundries like Samsung in securing AI chip orders, and the future financing and IPO timelines for AI startups. Analysts have recently suggested that OpenAI and Anthropic should not delay their IPOs for too long, partly because developing custom chips and computing infrastructure requires substantial long-term capital.
Primary Motivation: Securing Computing Control
Currently, training and running large AI models demands vast amounts of high-performance computing resources. The AI computing market is heavily reliant on Nvidia's GPU architecture, and tight supply has kept model training and inference costs high. For model companies like OpenAI and Anthropic, chips are no longer mere procurement items but core means of production.
Demand for Anthropic's Claude model is projected to grow significantly by 2026. Reports indicate the company's annualized revenue has surpassed $30 billion, up from approximately $9 billion at the end of 2025. This business expansion is driving a rapid increase in computing needs, amplifying the operational impact of uncertainties in external chip supply.
Anthropic currently relies on various third-party chip solutions, including TPUs designed by Alphabet's Google and Amazon's custom chips. The company also has a long-term TPU supply agreement with Google and Broadcom, linked to a previously announced $50 billion U.S. computing infrastructure investment plan.
This means that developing in-house chips does not equate to a complete break from external suppliers. A more realistic goal is to gain core design capabilities, create technical alternatives, and strengthen their negotiating position in future commercial deals.
Beyond Cost Savings: The Crucial Role of Hardware-Software Synergy
The most immediate rationale for custom chips is cost reduction. By designing custom ASICs, AI companies can optimize the computing workflow for their specific model architectures, eliminating unnecessary modules found in general-purpose chips, thereby improving energy efficiency. If Anthropic's chip is successfully produced and deployed, it could significantly lower API call costs and influence pricing structures in the enterprise AI applications market.
However, cost is not the only factor. Industry analysts emphasize that the greatest potential for AI efficiency gains lies not just in faster chips, but in co-design across the model, software kernel, and silicon layers. They note that optimizing a single layer might yield a two-fold improvement, but cross-layer co-design could deliver results far greater than a simple multiplication of individual gains.
This explains why both OpenAI and Anthropic are moving toward deeper hardware involvement. Model architectures are not inherently suited to all chips. The models from OpenAI and Anthropic have significant differences in aspects like matrix multiplication unit size, attention mechanism structure, and expert layer shape, which naturally inclines each company toward different hardware paths. For instance, using TPUs might be a poor decision for OpenAI's model trajectory, while using GPUs for training could be suboptimal for Anthropic and Google's model direction.
In essence, custom chip development is not merely about replacing Nvidia GPUs with in-house alternatives. The true objective is to enable models to be designed from the ground up to align with the underlying hardware, thereby improving inference speed, energy consumption, throughput, and unit economics.
Not an Immediate Replacement for Nvidia, but a Long-Term Counterbalance
The journey from R&D and design to final production and deployment of a custom AI chip typically takes 18 to 24 months. Even if Anthropic successfully partners with Samsung, its custom chip is unlikely to substantially replace existing computing supply in the short term.
OpenAI started earlier. Reports suggest OpenAI is collaborating with Broadcom and TSMC, with plans to deploy its first inference chip in the second half of 2026. Compared to Anthropic, OpenAI is more proactive and closer to the deployment phase on its custom chip path.
While the direction of major model companies developing their own chips points toward reducing reliance on suppliers like Nvidia, it does not mean Nvidia's position will be rapidly eroded. Nvidia GPUs retain advantages in versatility, and much of the model and open-source ecosystem is already optimized for them. The so-called CUDA moat is not just the software itself but the vast array of downstream models and software ecosystems already adapted to Nvidia's hardware architecture. If a model's expert structure, hidden dimensions, and communication patterns are better suited to GPUs, migration to other chips may not be straightforward, even if they offer advantages.
Therefore, custom chip development is more about establishing a second pathway. OpenAI and Anthropic are likely to continue using a mix of GPUs, TPUs, and other computing resources while deploying their custom ASICs for more specific, stable, and high-frequency workloads, particularly in inference scenarios.
The Broader Industry-Wide Race for Computing Autonomy
The shared logic behind OpenAI and Anthropic's custom chip efforts can be summarized in three points: reducing long-term computing costs, decreasing dependence on external supply, and enhancing model efficiency through hardware-software co-design.
The third point may be the most critical. As model companies scale, general-purpose computing cannot fully meet the needs of their differentiated architectures. Custom chips allow a company to place model design, system software, and underlying silicon within a single optimization framework.
The direction is clear: competition in large AI models is extending from "whose model is stronger" to "who can better control computing power, capital, and the hardware stack." This is the fundamental reason both OpenAI and Anthropic are pursuing custom silicon.
Anthropic's exploration is not an isolated case. From Google's decade-long development of its TPU series, to Amazon's Trainium series focused on training, to Meta's MTIA series for inference, and Microsoft's ongoing Maia series, leading tech companies are deeply invested in the custom chip race.
For Samsung, securing a chip manufacturing order from Anthropic would significantly boost the influence of its foundry business in the AI sector. Samsung is currently fiercely competing with rivals like TSMC for advanced-node clients. Bringing in a high-growth-potential AI customer like Anthropic would help expand its footprint in the AI semiconductor landscape.
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