At AINNA, I spend most of my time deploying neural 系统 in real operational environments, not labs. 时间 you run AI in production, 令牌 are not just an LLM billing metric. They are a direct 测量 of GPU cycles, memory pressure, latency and burn rate. Every extra token is more compute you have to pay for.
We started at 34 billion 令牌 across the 系统. After moving to a detached 系统 architecture, usage fell to around 1.5 billion. With process segmentation and modular refactoring, we cut that again by roughly 50% from 1.5 billion.
This is not prompt tuning. It is 系统 设计. Most AI cost is wasted because the model is forced to re-read the same domain, the same schemas, the same logic and the same 工作流 again and again for every small change.
But when the 系统 is detached and every process owns its own dictionary and contract, the AI only loads what it needs. Modify the payment flow? Pull the payment context. Modify order checking? Pull the order context. No full-系统 scan. No token bonfire.
This is where 中小企业 can win. AI should not be a privilege reserved for companies with giant servers and giant budgets. The future is not just bigger models. The future is 系统 that feed the model exactly what it needs, and nothing more.
