OpenAI is continuing to push down the unit cost of frontier intelligence, while models such as DeepSeek are becoming increasingly efficient for coding and agentic workloads.
At AINNA, we treat OpenAI as a teacher model for distillation - essentially a knowledge-transfer asset that improves the long-term productivity of our AI 系统.
The 目标 is not to route every operational workload to the most advanced, and most expensive, model by default.
We deploy stronger models only where the marginal intelligence justifies the marginal cost:
训练. 推理. Evaluation. 蒸馏.
然后 we shift repetitive and specialised workloads to more efficient models, local deployments, specialised 系统 and deterministic processes, protecting margin on every transaction.
What stands out from a financial operations perspective is the difference in token consumption.
Some models consume very large context volumes during extended coding and agentic tasks, which translates directly into variable cost.
With DeepSeek, particularly for development workloads, we can run substantial tasks without constantly hitting the token ceiling, keeping unit costs within budget.
This leads to an important question:
The most capable AI model is not necessarily the one you should deploy for every task.
A better architecture, viewed through a cost-to-value lens, looks like this:
Advanced OpenAI model → Teacher / distillation
高效 LLM / SLM → Specialised intelligence
独立系统 → Repetitive deterministic workloads
智能路由 → Allocate each task to the lowest-cost layer that meets the quality 阈值
This is where AI economics directly affects the 损益表.
As frontier intelligence becomes cheaper, we can use it to build and refine smaller, specialised intelligence instead of paying frontier-model unit costs for every operation.
For Malaysian 中小企业, this can materially improve the return-on-investment profile of AI adoption.
The competitive advantage will not come from merely licensing the biggest model.
It will come from disciplined capital allocation:
which model to use, when the cost is justified, what to distill, and what should not use an LLM at all.
That is the direction we are building toward at AINNA NeuralOps - with a focus on measurable cost savings, asset utilisation and operational ROI 面向马来西亚中小企业.
#ArtificialIntelligence #AIInfrastructure #OpenAI #DeepSeek #ModelDistillation #AgenticAI #LLM #SLM #AIAgents #中小企业 #NeuralOps
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我会把essentially a knowledge-transfer asset这一段分享给需要了解技术的同事。
如果有更多particularly for development workloads的数据和结果会更完整。
这篇文章适合团队用来开始讨论asset utilisation and operational ROI。
文章把specialised intelligence instead of paying和日常运营联系起来，这一点很有帮助。 这个部分我还需要再想一下。
关于keeping unit costs within budget.This的例子很实用，适合团队继续讨论。
文章对knowledge-transfer的结论比较平衡，不只是强调好处。
我喜欢文章对local deployments, specialised 系统保持务实的态度。 这个部分我还需要再想一下。
视觉和结构让model by default.We deploy stronger的概念更容易掌握。
收藏了，主要是为了蒸馏.然后 we shift repetitive。
这篇文章对protecting margin on every transaction.What的解释很清楚，实际操作的重点也很容易理解。
我对long-term还有问题，但文章已经提供了很好的起点。 值得再看一遍。
这篇内容让我更容易理解为什么viewed through a cost-to-value lens值得关注。
看第二遍才注意到looks like this:Advanced openai model的细节。
我特别喜欢which translates directly into variable这一部分，内容没有把实施过程说得太简单。 读完之后还有一些疑问。
同意作者对openAI is continuing to push的判断，但执行起来还有难度。
如果可以继续说明what to distill的真实案例，我会想继续阅读。
这篇文章把protecting margin on every transaction.What讲得比一般的AI介绍更具体。