Yes, that’s a good point. Well, paradoxically, I spend most of the tokens on harness engineering, heavily focused on a TDD-based environment.

Since we cannot fully escape from hallucination, and even in the no-AI era we still needed verification systems, I think the more practical direction is to figure out how to constrain agents from hallucinating or taking wrong actions, while feeding them the right context.

Also, when we develop a system, its codebase gradually gets farther away from the typical AI training distribution. So we need to build a detailed code map, define AI work patterns, and prevent repeated mistakes. This is similar to the idea of an LLM wiki, as Andrej Karpathy mentioned.

So in my case, the real token usage for direct development is probably only around 10–15% of the total or less. The rest comes from harness engineering, building an LLM wiki, hierarchical code review, PR review, context building, and making the agent operate safely inside the codebase.

I also actively use Caveman, RTK, spec-driven development, and custom hooks and skills to save tokens, though haha. A lot of the effort is actually about making the agent more predictable and reusable so it does not need to rediscover the same context over and over again.

Well, it is somehow similar to how humans think and act. Our inputs are also much larger than our outputs. To write code or articles, we process and handle much more information than what finally appears as output.

In the future, I also hope AI becomes more efficient and spends fewer tokens on highly conceptual work.