We don't necessarily see many China cars in so-called "developed countries".
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Not because demand is slowing. Because costs are becoming impossible to ignore. Here are the numbers: 📈 Chinese AI models now process ~18.5 trillion tokens per week on OpenRouter. 🇺🇸 US models? Around 6 trillion. That's a 3x gap. Why? • Lower energy costs. • More efficient models. • Aggressive pricing that's reshaping the competitive landscape. Meanwhile, something interesting is happening inside large enterprises. Companies including Amazon, Walmart, Cisco, Uber, and Meta are reportedly introducing internal limits on AI usage as spending exceeds expectations. One striking example: A software company saw its AI bill jump 7x overnight after moving from a flat-rate subscription to usage-based pricing. Suddenly, the true cost of AI became visible. And this is only the beginning. 📊 Goldman Sachs estimates AI agents could increase token consumption by 24x by 2030. That creates a fundamental challenge: AI demand may keep exploding... while AI budgets become increasingly constrained. The next competitive advantage won't just be building the smartest models. It will be building the most cost-efficient ones. The AI story is evolving: ➡️ From "Who has the biggest model?" ➡️ To "Who delivers the lowest cost per useful output?" The winners of the next AI wave may not be those with the most compute... ...but those who make intelligence affordable at scale. Do you think AI spending is finally reaching a reality check, or is this just a temporary pause before the next investment wave? Source: FT, Global Markets Investors
Chinese domestic demand accounts for most of that growth. Source: Azeem Azhar
DeepSeek’s massive price cuts have made its AI token costs up to 50x cheaper than OpenAI and Anthropic, reshaping enterprise AI economics. Since hashtag#AI costs scale with token usage, companies running coding agents or reasoning-heavy models can spend millions—or even billions—annually. More advanced models consume huge hidden “reasoning” tokens, dramatically increasing compute costs. This is pushing firms toward cheaper, optimized models and tools, with companies like Microsoft and Uber already feeling budget pressure. The key competitive advantage in AI may shift from having the smartest model to delivering “good enough” AI at the lowest scalable cost. Source: Bull Theory