今天, I am building no fewer than three detached 系统 every day, in addition to producing around four complete pitch decks weekly, with extensive support from AI 智能体.
Across development, research, coding, testing, documentation, analysis and iteration, our AI workload can reach 3–5 billion 令牌 per month.
If translated into commercial premium LLM API pricing, the equivalent cost could easily reach hundreds of thousands of ringgit monthly.
Now translate that into a corporate environment.
想象 an organisation with hundreds or thousands of employees, each equipped with AI 智能体 for research, analysis, reporting, coding, documentation, operations and decision support.
令牌消耗 would scale extremely fast.
At the same time, corporations increasingly have little choice but to adopt AI 智能体.
Why?
Because small companies like ours can now achieve productivity levels that previously required hundreds or even thousands of employees.
AI is rapidly narrowing the productivity gap between small companies and large corporations.
But there is another side.
AI productivity does not have to mean massive AI expenditure.
In our environment, even at around 3 billion 令牌 per month, direct AI cost can remain only about RM100–RM200 monthly.
The difference is not because we use less AI.
It is because we built a 智能路由 architecture around our AI 智能体.
Not every task should go to the largest or most expensive model.
Simple tasks → 轻量模型s.
Coding tasks → specialised models.
复杂推理 → stronger models.
确定性 validation → detached 系统.
Repetitive processing → automation and conventional computing.
The principle is simple:
Use premium intelligence only when it is truly required.
Our detached 系统 handle validation, calculations, filtering, reconciliation, rule-based decisions and structured processing that do not require an LLM.
This allows aggressive AI usage without paying as if every task needs the most powerful model.
For me, the future of 企业版 AI is not simply:
“Give every employee an AI agent.”
It should be:
“Give every employee an AI agent — but build intelligent infrastructure underneath it.”
Because when an organisation has 1,000 or 10,000 AI-enabled employees, the real question is no longer whether it uses AI.
The real question becomes:
How much intelligence is the organisation paying for that it never actually needed?
This is why we see 智能路由, specialised models and detached 系统 as more than optimisation.
They are becoming AI cost-control infrastructure.
At 企业 scale, that difference could be worth millions of ringgit yearly.
The next phase of AI adoption will not only be about the most powerful models.
It will be about knowing when not to use them.
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