“Chief Revenue Destroyer” — Jensen Huang deliberately obsoletes own products before anyone else does.
NVIDIA’s Annual Release Cadence
- Blackwell (2024-2025): B200: 2.5x inference vs Hopper, GB200 NVL72: 120kW per rack
- Vera Rubin (Q3 2026): HBM4 memory, NVLink 6, Vera CPU + Rubin GPU co-design
- Rubin Ultra (H2 2027): HBM4E, 3rd+ TB/s memory bandwidth
- Next Generation (2028): Cycle continues…
Generational Performance Leaps
- Hopper → Blackwell: 2.5x inference performance, 4x training efficiency
- GPT-4 Class Training: 25% cost reduction vs Hopper generation
- Energy per Token: 5x better efficiency (Blackwell vs Hopper)
The Jevons Paradox in Action
Historical Pattern: Every computing efficiency gain has increased total compute consumption, not reduced it.
DeepSeek Implication: 10x efficiency gains → 10x more use cases → More applications, not less infrastructure
Model Proliferation Drives Demand
- Free Models: 100+ open source releases (Nemotron, Cosmos, Alpamayo, GROOT)
- Llama Downloads: 700M+ and growing
- Each Model = Future GPU Demand: Every deployment requires training, fine-tuning, and inference compute
Competitor Time Gap
- Custom Silicon: 3-5 years from design to production
- NVIDIA Cadence: ~1 year between new architectures
By the time competitors match H100, NVIDIA ships B200. By the time they match B200, Rubin arrives.
Why Competitors Can’t Catch Up
- Moving Target: By the time competitors match H100, NVIDIA ships B200
- Full-Stack Optimization: Hardware + CUDA + libraries + frameworks all advance together
- Ecosystem Lock-In Compounds: Each generation adds more CUDA-optimized code to global codebase
The Speed Moat: Innovation velocity creates perpetual gap competitors cannot close.
This is part of a comprehensive analysis. Read the full analysis on The Business Engineer.
