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hot_img SemiAnalysis: Gemini has exited the frontier competition, and GCP is accelerating the sale of TPUs to third parties for profit

The research organization SemiAnalysis released an analysis indicating that Google DeepMind is no longer among the leading AI laboratories. A week prior, DeepMind co-founder Demis Hassabis stepped back from daily operations, and key members such as Google Chief Scientist Jeff Dean and Gemini co-lead Oriol Vinyals left to establish a new lab called Discovery Loop. The analysis suggests that the long-term struggle within Google over computing power allocation between Gemini and GCP has concluded with GCP emerging victorious.SemiAnalysis stated that Gemini 3.5 Pro has been canceled, and Gemini 3.6 Flash's performance is inferior to that of leading Chinese open-source models and Grok 4.5. Currently, Gemini has fallen to the 8th or 9th position in the large model rankings. Meanwhile, GCP is selling a large number of TPUs to competitors like Anthropic, having secured long-term leasing and sales contracts for hundreds of thousands of TPUs over the past nine months. The Tokenomics model estimates that Gemini's own ARR is about $12 billion, while GCP's third-party AI cloud service revenue is expected to exceed $73 billion by the end of 2027, with TPU system sales contributing an additional over $120 billion. GCP's latest quarterly growth rate is 82%, and it is expected to accelerate to over 100% by 2027 due to TPU system sales, contributing approximately $3 to Google's earnings per share.

Western Digital CPO: Flash memory handles the present, HDD manages the entire lifecycle

Ahmed Shihab, Chief Product Officer of Western Digital, stated that the key to AI storage competition is not simply pursuing the fastest medium, but whether it can continuously scale capacity at an affordable cost. He believes that many architectures run well in the early stages, but when the data scale grows from several PB to hundreds of PB or even EB levels, issues may arise due to uncontrolled costs.Flash storage is suitable for high-performance, low-latency scenarios such as model weights, GPU overflow, KV caches, and session contexts; HDDs are more suitable for large-scale, long-term storage, and cost-sensitive data such as training corpora, logs, checkpoints, compliance records, synthetic data, and inference outputs. He summarized: "Flash handles the present, HDD handles the entire lifecycle."At the scale of AI, storage costs themselves will become an architectural issue. Long-term storage of large volumes of data on high-performance media will crowd out budgets for computing, networking, power, and personnel. For many bulk storage workloads, the issue is not whether flash can store, but whether customers can afford the total cost of using flash in the long term.In the future, AI storage will not be dominated by a single technology, but should be designed in layers based on performance, cost, power consumption, density, reliability, and data lifecycle. He emphasized that this is not a competition between flash and HDD, but rather a need to match the appropriate medium for different workloads from the very beginning; otherwise, the architecture may impact business sustainability as it scales.

NVIDIA announces the mass production of Co-Packaged Optics (CPO), related concept stocks may see a rebound

Gilad Shainer, Senior Vice President of NVIDIA, announced at a recent technology forum that CPO has entered the mass production stage. The switches co-developed with the supply chain have begun delivery to closely collaborating customers and are being deployed in-house. It is expected that starting this year, switches equipped with CPO technology will be widely adopted in AI factories around the world.Shainer pointed out that the biggest opportunity in the future optical communication market lies in vertical scaling, which will require bandwidth efficiency more than ten times that of horizontal scaling. Previously, Shainer was responsible for R&D at Mellanox, and after the company was acquired by NVIDIA, he led the AI platform network transmission department and co-developed the COUPE silicon photonics packaging platform with TSMC. This announcement helps to dispel market concerns about the upgrade of optical communication specifications and boosts the operational outlook for related manufacturers.It is reported that NVIDIA has already implemented CPO on Spectrum-X, aiming to align with the upcoming large-scale deployment of the Vera Rubin AI platform (which has a computing speed at least three times faster than GB300). Trendforce estimates that the CPO/NPO market size will exceed $39 billion by 2030, with significant acceleration in growth momentum from 2028 to 2029 as optical interconnects are introduced with Scale-up.
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