In a development that could reshape the landscape of artificial intelligence hardware, Google is reportedly in advanced talks with Samsung Electronics to produce a key component of its upcoming AI processor. The Information detailed on Thursday, citing two individuals with direct knowledge of the matter, that Alphabet’s Google is looking beyond its traditional manufacturing partners to secure a more resilient and diversified supply chain for its next-generation tensor processing unit. The chip, internally codenamed “Icefish,” represents Google’s latest push to compete more directly with Nvidia’s dominant graphics processors in the rapidly expanding AI market.
Under the proposed arrangement, Taiwan Semiconductor Manufacturing Company, or TSMC, would continue to manufacture the primary computing core of the Icefish TPU. Samsung, however, would step in to produce a crucial secondary component that connects the processor to its memory. What makes Samsung’s potential role especially significant is that it would use the company’s advanced 2‑nanometer production technology. For those who have followed semiconductor trends over the past couple of years, the shift to 2nm is not merely an incremental improvement. It allows engineers to pack more transistors into a smaller physical space, which directly translates into better power efficiency, faster data processing, and improved performance for AI workloads. In practical terms, a chip built on a 2nm process can run complex large language models or handle real‑time inference tasks while generating less heat and consuming less energy compared to older nodes.
Beyond the manufacturing split between TSMC and Samsung, Google is also working with the chip design firm MediaTek on the architecture of Icefish. The processor remains in active development, and mass production is not expected before 2028. That timeline may seem distant, but in the world of custom silicon, a four‑year horizon is quite reasonable. Designing a new AI chip from the ground up, validating its performance across thousands of simulated workloads, and then aligning foundry capacity for high‑volume production typically takes years, not months. So when we hear that Icefish is still in development, it signals that Google is being deliberate rather than rushed.
For Samsung, winning even a partial order from Google would be a meaningful validation of its contract chip‑manufacturing ambitions. The company has been investing heavily to catch up with TSMC, the undisputed leader in advanced semiconductor fabrication. Samsung’s 2nm process is central to that strategy. In April, Samsung disclosed that it expected to secure more customers for chips made using this technology and was considering building a second plant in Texas to expand production capacity. Then in July 2025, Samsung landed a massive $16.5 billion deal with Tesla to produce AI chips using the same 2nm process. Adding Google to that client list would further cement Samsung’s position as a credible alternative to TSMC for cutting‑edge AI hardware.
At the same time, this move fits into a broader pattern of Google trying to reduce its reliance on a single chip manufacturer. TSMC currently dominates the market for advanced AI chips, but its facilities are running at full capacity trying to keep up with surging demand from every corner of the tech industry. That demand has become so intense that some analysts worry TSMC could become a bottleneck, limiting how quickly companies like Google can scale their AI infrastructure. By splitting production between TSMC and Samsung, Google gains valuable flexibility. If one foundry faces delays or capacity constraints, the other can help pick up the slack.
It is worth noting that this is not the only manufacturing relationship Google has been exploring. The Information reported earlier this week that Google was also in talks with Intel to produce more than three million TPUs in 2028. Taken together, these reports paint a picture of a company actively diversifying its supply chain rather than putting all its eggs in one basket.
On the design and strategy side, Google’s in‑house TPUs have quietly become one of the more credible alternatives to Nvidia’s industry‑standard GPUs. While Nvidia still commands the lion’s share of the AI chip market, Google’s TPUs have been gaining traction, especially within its own cloud business. Rising sales of TPUs have become a genuine growth driver for Google Cloud, allowing the company to offer AI compute capacity to external customers while reducing its dependence on Nvidia hardware. In April, Google unveiled two new custom chips designed specifically for training AI models and running inference, showing that its internal silicon team continues to push forward aggressively.
Despite the flurry of activity, there are still open questions. Samsung Electronics declined to comment on the report, and Alphabet did not immediately respond to a request for comment. Reuters noted that it could not independently verify the details. That leaves room for healthy skepticism. Negotiations of this scale can fall apart over pricing, technology transfer terms, or production yields. Samsung’s 2nm process, while promising, still needs to prove it can deliver the kind of reliability and volume that a customer like Google demands.
Public perception around this potential partnership is mixed. On one hand, analysts view it as a smart hedge against TSMC’s capacity constraints. On the other hand, some industry veterans worry that splitting production across two different foundries with different process technologies could introduce complexity in testing, packaging, and long‑term maintenance. There is also the question of whether Google’s Icefish, even with a 2nm‑manufactured memory interface, can truly close the performance gap with Nvidia’s next‑generation offerings expected around the same 2028 timeframe.
