Post by Vinija Jain

🪙 Tokenmaxxing vs. Valuemaxxing Tokenmaxxing is what happens when teams optimize for AI usage instead of workflow outcomes. It means treating token volume as a proxy for progress: more prompts, longer contexts, more agent runs, more expensive models, more background inference. The metric is easy to count, but it does not say much about whether the workflow became cheaper, faster, more reliable, or more useful. The failure mode is usually simple: deterministic work gets routed through probabilistic systems. 🗃️ A lot of business workflows are mostly structured logic. A lead comes in, fields are checked, a CRM record is updated, an owner is assigned, a Slack message is sent, and a follow-up task is created. Those steps do not need a language model. They need rules, state, retries, permissions, and observability. AI is useful in the parts of the workflow that require interpretation: summarizing messy text, extracting fields from unstructured inputs, classifying intent, drafting a response, analyzing tradeoffs, or deciding what context matters. That is where inference earns its cost. This is where Zapier’s valuemaxxing framing is useful: ⚙️ automate the structured steps, 🧠 use AI for the judgment-heavy ones, 🔀 keep model choice flexible, and 📈 measure business outcomes instead of tokens burned. Concretely, Zapier can route leads, update systems of record, trigger approvals, move data between apps, and handle predictable if/then logic without calling a model. Then, when the workflow hits an ambiguous step, like summarizing a customer note or classifying an inbound request, you can insert an AI step only there. That architecture matters because agentic workflows multiply cost. Every retry, tool call, summary, and follow-up can add more tokens. If the loop is built entirely around model calls, cost and latency scale with the loop. If the loop is built around automation plus selective AI, the model is used where it changes the result. Zapier’s valuemaxxing guide: https://bit.ly/4b0qtrH #ZapierPartner