Let's expose the hidden line item in your AI strategy: Tokenomics 💸
As we enter day 7 of building our AI accounting agents in public, we're tracking not just accuracy, but a crucial metric every leader must understand: the unit economics of every automated task.
The consumer AI experience masks this reality. A $20/month subscription is a deliberately limited entry point. Serious business/enterprise automation runs on consumption-based APIs, where costs are directly tied to usage and measured in tokens (or pieces of words).
So what does this mean? Every AI interaction has a cost for both input tokens (the context you provide) and output tokens (the AI's response).
In our case with building automation with accounting AI agent, this means a business with higher complexity like a larger chart of accounts, more legal entities, or intricate revenue rules, will have fundamentally higher AI operating costs. The input cost for every single automation request is larger before the agent even begins its work. More edge cases mean more code to read and more code to write, increasing both input and output costs.
This is why a smart AI strategy isn't just about buying a powerful model. It's about designing a data and process architecture (for example, our Architectural Guardrails that I keep referring to) that is ruthlessly efficient. Ultimately, the most powerful AI won't win; the most efficient architecture will.
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