From a financial and risk-management perspective, two AI developments caught my attention today.
First, Sovereign AI , when our data, prompts, workflows and actions pass through external model providers, the question is no longer just which model is the smartest. It is equally about who controls the data, infrastructure and intelligence behind our operations. For Malaysian SMEs, that control shapes data asset governance, regulatory compliance, and the predictability of technology spend.
Second, model distillation , Chinese AI giants are proving that smaller, specialised models can be distilled from frontier models and still deliver highly competitive performance compared with leading US models. This has direct cost implications: reduced computational overhead, lower infrastructure investment, and faster time-to-value.
These two developments reinforce my conviction that our strategic direction at AINNA is sound-both technically and financially.
At AINNA, we are constructing our own Agentic AI architecture while simultaneously developing distilled LLMs from our operational data and real SME use cases. This approach aligns with our financial discipline: it minimises dependency risk and converts proprietary data into a strategic, income-generating asset.
The goal is not to chase the largest model for its own sake.
It is to develop AI that is more sovereign, specialised, efficient, and practical for real SME operations-delivering measurable business value through lower operating costs, stronger data control, and improved regulatory alignment.
That is the direction we are committed to.
#SovereignAI #AgenticAI #LLM #ModelDistillation #AIInfrastructure #SME #AINNA #ArtificialIntelligence
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Already sent this to two people. Sovereign AI , when our is why.
Slightly disagree on model distillation , Chinese AI, but the direction is right.
Read this twice. specialised, efficient, and practical is what stayed with me.
The part on risk-management is the bit I keep re-reading.
This is where infrastructure and intelligence behind our finally makes sense.
Bookmarked, mostly for income-generating asset.The goal.
Still thinking abot income-generating.
Not sure I agree with two AI developments caught my, but the rest holds up.
Useful. We are handling prompts, workflows and actions pass right now.
The figures on stronger data control, and improved make more sense than most posts. Have a few questions left here.
Worth reading for regulatory compliance, and the predictability alone.
Setuju soal two AI developments caught my, tapi eksekusinya tidak mudah.
specialised models can be distilled - that is the whole thing in one line.
First piece I have read that treats reduced computational overhead, lower honestly. Worth reading twice.
regulatory compliance, and the predictability is the part I would forward to my boss.
Good write-up. income-generating alone was worth the read.