What's in this episode:
The tokenminning.com origin story — (As seen in the NYT) The Tokenminning Manifesto by Neurometric is gaining traction, with coverage in the NYT. As enterprises prove out AI use cases while blowing through their token budgets, token engineering is becoming a first-order concern.
Specialized models vs. frontier models — Small, task-specific models can almost always beat a frontier model on a given task, even at a fraction of the parameter count. The catch: that specialized model can only do that one thing well. This is a feature, not a bug.
The new Token Engineering Platform — Neurometric's answer to a fragmented tooling landscape. It brings SLM fine-tuning, distillation, and token caching into a single system so teams get one view across their entire model stack.
COGS vs. OPEX — how Neurometric segments its customer base, and why companies whose AI spend hits gross margin (not just headcount-adjacent budgets) are the ones moving fastest toward token efficiency.
Listen to the full episode: https://tokenengineering.podbean.com/
Watch on YouTube: https://youtu.be/JHFeraXq3RU?si=fzzRcEBuPo3vbF6A
**Resources mentioned:**
Tokenminning Manifesto: tokenminning.com
Neurometric AI: neurometric.ai
NYT Article: https://www.nytimes.com/2026/06/18/technology/ai-token-minimizing.html
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