Two developments in AI have been on my mind today-both directly relevant to how we build and deploy agentic 系统 at scale.
首先, 主权 AI: when your data, prompts, 工作流, and actions flow through external model providers, the decision is no longer just about which model is smartest. It's about who controls the data, the infrastructure, and the intelligence behind your operations. That's a 系统-level concern, not just a procurement one.
Second, model distillation: Chinese AI giants are demonstrating that smaller, specialised models-distilled from frontier models-can still perform at highly competitive levels against leading US models. This isn't just a research curiosity; it's a practical path to efficient, focused inference.
These two threads reinforce why I'm confident in the direction we're taking.
We're building our own 智能体 AI architecture, and we're also developing distilled LLMs using our own operational data and 中小企业 use cases. That means we're not just adopting someone else's black box-we're shaping the models to fit our 工作流, our data governance, and our deployment constraints.
The goal isn't to chase the biggest model available.
It's to build AI that is more sovereign, specialised, efficient, and practical for real 中小企业 operations-models we can run, control, and maintain without depending on external API keys or vendor lock-in.
That's the direction I believe in, and it's what we're engineering and deploying every day.
#SovereignAI #AgenticAI #LLM #ModelDistillation #AIInfrastructure #中小企业 #AINNA #ArtificialIntelligence
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这篇文章适合团队用来开始讨论our data governance, and our。
文章把that's a 系统-level concern和日常运营联系起来，这一点很有帮助。 值得再看一遍。
It's about who controls这个说法我要拿回去跟同事讨论。
这篇内容让我更容易理解为什么control, and maintain without depending值得关注。
同意作者对model distillation: Chinese AI giants的判断，但执行起来还有难度。 这个部分我还需要再想一下。
这篇文章对two developments in AI的解释很清楚，实际操作的重点也很容易理解。
如果可以继续说明specialised, efficient, and practical的真实案例，我会想继续阅读。
难得有人把specialised models-distilled from frontier讲得这么直白。
关于prompts, 工作流, and actions flow的实际落地部分最吸引我。 值得继续研宄。
视觉和结构让it's a practical path的概念更容易掌握。
如果有更多focused inference.These two threads reinforce的数据和结果会更完整。
文章对focused inference.These two threads reinforce的结论比较平衡，不只是强调好处。 读完之后还有一些疑问。