It may come from using less AI, more intelligently.
That distinction is becoming increasingly important as companies move from AI experimentation to production. The goal is not to make every step of a workflow dependent on a model. The goal is to design systems that use AI where it creates the most value, and deterministic software everywhere else.
Our thesis is simple:
Use AI aggressively to build systems, automation, and logic. Once the system is mature, let deterministic software handle repetitive work 24/7. Bring AI back only when genuine reasoning is required.
We saw this shift firsthand. Our usage dropped from approximately 32 billion tokens during development to around 3 billion tokens per month after the core system became operational.
That is more than an efficiency gain. It changes the economics of AI.
Less compute.
Lower operating costs.
Greater scalability.
Stronger margins.
More sustainable infrastructure.
The principle is straightforward:
Use AI to build the machine. Then let the machine do the work.
Companies that learn to scale intelligence without scaling compute at the same rate could create a significant competitive advantage. Over time, that advantage could translate into billions of dollars in future enterprise and investment value.
The future of AI will not be defined solely by bigger models or more tokens.
It will be defined by better architecture, and by knowing when not to use AI at all.
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The figures on our thesis is simple: use make more sense than most posts. It make the point easier to understand.
Saya kurang setuju sikit pasal 24, tapi arah dia betul.
Read this twice. handle repetitive work 24 is what stayed with me.
Useful. We are dealing with once the system is mature right now.
Bookmarked, mostly for our usage dropped. Worth reading twice.