🧮 Token Deflation: Why China's Cheaper AI Isn't Bearish for the Supercycle Key Takeaways: 📍Chinese open-weight models have rapidly closed the performance gap with US frontier labs, now trailing by only a few weeks in reasoning and coding benchmarks. 📍These models offer a significant cost advantage, priced at one-third to one-fifth of their US counterparts due to structural factors like cheaper electricity and architectural efficiency. 📍China is employing a deliberate industrial strategy to commoditize the AI model layer by open-sourcing weights to capture downstream monetization in cloud and advertising. 📍The Jevons Paradox: Lower AI token prices expand the market by making high-volume use cases viable, ultimately driving higher compute demand. 📍Sustainable enterprise value remains concentrated in the US-aligned supply chain, particularly in physical bottlenecks like semiconductors, advanced packaging, and power management. 📍Chinese models win token volume; US models win enterprise dollars on privacy and reliability. Price wars at home and blocked global monetization squeeze Chinese labs financially. 📥 Our latest report walks through the power-cost gap, the Vercel token-vs-spend split, and what's actually left for non-US, non-China labs 👇 https://t.co/7q59Y2z1sx