Infographic
Zero Token Architecture - infographic - by Vinay C
This weekend I watched this talk on Zero Token Architecture:
https://youtu.be/A7WFt2JQ5sg?si=WAmV5J_sFHsoKvUH
The name sounds like another AI buzzword, but the core idea is surprisingly practical.
We often assume that if an LLM can solve a task, it should sit in the request path and solve it every single time.
But what if the LLM only needs to solve it once?
Instead of asking the model repeatedly, use it to generate an artifact that can run thousands of times on its own.
For example:
- Generate a Python script once, then run it on every log file.
- Generate a SQL query once, then schedule it every day.
- Generate linting or static analysis rules once, then use them on every pull request.
- Generate a workflow or configuration once, then execute it without another LLM call.
That’s the part that really changed my perspective.
Sometimes the best use of AI is to remove future AI calls.
That isn’t just about saving tokens.
It also means:
- Lower latency
- More predictable behavior
- Easier testing and debugging
- No dependency on a model for deterministic tasks
- The option to run offline
Of course, not everything fits this approach. If you’re working with natural language, new documents, ambiguity, or reasoning, runtime inference is exactly the right tool.
The lesson I took away wasn’t “use less AI.”
It was to pause before adding an LLM to the critical path and ask:
- Can a Bash script do this?
- Can SQL solve it?
- Can a regex or deterministic program handle it?
If the answer is yes, let the LLM help you build the solution once—then get out of its way.
The goal isn’t to build AI-first systems. It’s to build the simplest system that solves the problem well. Sometimes that includes an LLM. Sometimes it doesn’t.
One sentence from this idea has stayed with me:
The most valuable LLM inference is often the one you never have to repeat.
Has anyone else explored this way of designing AI systems?
Originally published on LinkedIn.