AI Competition's Essence Is Chip Procurement
The 2024-2026 AI competition is an era decided not just by model smartness but by "how much compute you can amass." OpenAI, Anthropic, Google, Meta, xAI secure GPU/TPU at hundreds-of-billions-of-yen scale, and governments advance trillion-yen-scale investment in sovereign-AI strategies.
NVIDIA's Dominance and Challengers
NVIDIA H100 / H200 / B100 / B200 / GB200
The Hopper generation (H100) in 2024, successor H200, and from 2025 the Blackwell generation (B100, B200, GB200) mass-produced. $40-50k per card, one Blackwell GPU rack (GB200 NVL72) on the order of 100M yen. Supply tightness makes 6-12-month waits the norm.
Google TPU v5/v6
Google's in-house TPU updated v5p (2023), v5e (2024), v6 Trillium (2025), v6e (2026). Supports Gemini training/inference. Also provided externally via GCP (Anthropic, various startups).
AWS Trainium / Inferentia
AWS's AI-dedicated chips. Trainium2 (2024) for training, Inferentia2 for inference. Large-scale adoption in exchange for investing in Anthropic. Targets 30-50% cheaper than H100 on cost-performance.
Microsoft Maia / Cobalt
Microsoft's in-house AI chips announced in 2023. For OpenAI-model operation and Azure customers. Aims to reduce NVIDIA dependence and improve Azure margins.
Dedicated Inference ASICs
- Groq LPU: ultra-low-latency inference-dedicated. Llama 3 over 500 tokens/sec
- Cerebras WSE-3: a giant AI chip where 1 chip = wafer-sized
- SambaNova: enterprise-dedicated
- Etched Sohu: a Transformer-dedicated ASIC
China's Situation
US export controls ban H100/H200 export to China. NVIDIA sold regulation-compliant versions (H800, H20, L20, L2) but controls tightened from 2024. China's response:
- Huawei Ascend 910B/910C: performance 60-80% of H100
- Startups like Biren, Moore Threads
- SMIC (China's TSMC equivalent) strengthening 7nm/5nm manufacturing
- DeepSeek etc. proving "frontier models possible even with regulation-compliant versions"
