GOOG’s Optical Edge: OCS, Gemini 3, Model Progress, and the Coming Storage Crunch

The competition in large-scale AI models is consolidating into a three-way race among Google, OpenAI, and Anthropic. While OpenAI and Anthropic excel in consumer adoption and coding/agent workflows, Google is leveraging its unique TPU + Optical Circuit Switching (OCS) architecture to achieve decisive advantages in scalability, cost, and performance. The upcoming release of Gemini 3 — expected to outperform rivals across multimodality, cost efficiency, and ultra-long-context reasoning — positions Google to potentially claim leadership in overall model capabilities by year-end.

At the same time, a shift from pre-training to mid-training and the explosive rise of multimodal models are driving exponential data generation, creating unprecedented demand for enterprise-grade SSD infrastructure. These dynamics suggest a new cycle of storage shortages, capacity expansion, and supply chain reconfiguration — with NAND flash and advanced interconnect technologies as key beneficiaries.

Google’s Advantages in Model Technology and Hardware Architecture

In the coming month, the most anticipated model is Gemini 3, which we expect to be the standout release of the year. It is poised to deliver across speed, cost, and quality. Over the past few weeks, the Nano Banana model has already drawn widespread attention, signaling a significant leap forward in Google’s overall model capabilities. Over the last two years, Google has gone from being seen as an “AI laggard” to steadily catching up with OpenAI and Anthropic — and by year-end, it could well emerge as the leader in overall model performance.