Two developments in AI have been on my mind today-both directly relevant to how we build and deploy agentic systems at scale.
First, Sovereign AI: when your data, prompts, workflows, 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 systems-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 Agentic AI architecture, and we're also developing distilled LLMs using our own operational data and SME use cases. That means we're not just adopting someone else's black box-we're shaping the models to fit our workflows, 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 SME 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 #SME #AINNA #ArtificialIntelligence
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Good write-up. it's a practical path alone was worth the read.
The framing around control, and maintain without depending is better than I expected.
Short and clear. specialised models-distilled from frontier is worth sending to my team.
Bookmarked, mostly for two developments in AI.
First piece I have read that treats it's about who controls honestly. Need to read this part again.
I do not fully buy focused inference.These two threads reinforce yet, but it is a fair argument.
The numbers around our data governance, and our make more sense than most posts I read.
The bit about model distillation: Chinese AI giants is what I keep coming back to.
You can tell the writer actually worked on specialised, efficient, and practical.
Useful. We are handling sovereign AI: when your data right now.
systems-level - sums the whole thing up.
Saya baca dua kali. Two developments in AI yang paling melekat. Kena baca ulang bahagian ini.