Google's TPU Advances vs. Nvidia's AI Chip Dominance: Competitive Dynamics & Investment Insights

• According to IDC Report [0]: AI supercomputer customers achieve an average three-year ROI of 353% and reduce IT costs by 28%.
• According to ts2.tech [2]: Nvidia will hold approximately 60% of the AI data center accelerator market share in 2025, with the CUDA ecosystem forming its core moat.
• According to Forbes [5]: Google’s Ironwood TPU is 2-3 times more energy-efficient than Nvidia’s GPU, with a significant cost advantage in inference.
• According to The Motley Fool [1]: Anthropic signed an order for over 1 million TPUs, saving more than $2 billion annually in costs.

• Snowball User [6]: Google’s TPU is strong, but it relies on a closed technology stack and cannot challenge Nvidia’s CUDA ecosystem and global supply chain.
• Reddit Comment [7]: Bullish on computing power supply chain (optical modules like LITE, NAND flash) and energy infrastructure opportunities; negative for software companies with low bargaining power.

The launch of Google’s Ironwood TPU marks the rise of ASICs in the AI inference field, but Nvidia still dominates the training market with its CUDA ecosystem and general-purpose GPU advantages [2][5]. Anthropic’s large order validates the cost-effectiveness of TPUs, but Google still needs to purchase Nvidia GPUs to meet mixed workload demands [6]. Investment opportunities are concentrated in the computing power supply chain (SerDes, HBM, optical modules like LITE), NAND flash (e.g., SanDisk), and energy infrastructure (alleviating power supply bottlenecks) [5][6]. In the short term, Nvidia’s ecological barriers are difficult to break through; in the long term, the golden age of ASICs may arrive [0][5].