Token Engineering

Why Inference Time Compute Is the Future of AI

Token Engineering

Show Notes

Welcome to the very first episode of Inference Time Tactics — the podcast for builders, researchers, and engineers pushing the limits of AI performance. In this kickoff conversation, hosts Rob May and Cooper (co-founders of Neurometric AI) break down why inference time compute is emerging as the third scaling law of AI — and why it matters more than ever. They unpack:

What “inference time compute” really means (and how it differs from training and fine-tuning)

Why reasoning algorithms like best-of-N, chain of thought, and beam search are reshaping performance

How recent research — and OpenAI’s 2024 reasoning model — sparked an explosion of interest

The challenge of reliability (“three nines” and beyond) in multi-step agent workflows

Why open-source models may win big, and where inference fits at the edge

This is a technical, tactical deep-dive — but without the heavy math. If you’re building the next generation of AI systems, or just want to understand where the field is really headed, this episode is your starting point. 🔗 Learn more at neurometric.ai

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In this episode of Token Engineering, Cooper sits down with Yash, Head of AI Research at Neurometric AI, to break down TMax from AI2 (Allen Institute for AI)—a fully open dataset and training recipe that pushes small open-weight models like Qwen to near-frontier performance on terminal agent tasks. The paper argues that diversity and difficulty matter more than where the data comes from—even when that data is entirely invented by Gemini rather than pulled from real-world code.

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In this episode, Cooper sits down with Neurometric CEO, Rob May, to talk through a big-news month: the show's rebrand from Inference Time Tactics to Token Engineering, the launch of tokenminning.com as a response to the "tokenmaxxing" trend, Neurometric's new token engineering platform and fundraising announcement. But the bigger conversation is about why Rob thinks "token engineering" is about to become a discipline every AI company needs—the same way DevOps or UX became non-negotiable roles—and why he's betting the company on getting there first.

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In this episode of Inference Time Tactics, Cooper sits down with Yash Sharma, Head of AI Research at Neurometric AI, to break down the Pioneer Agent paper from Fastino Labs—a system that uses Claude Sonnet as an ML engineer in a box to build and improve small language models end-to-end. From cold start data curation to production failure diagnosis, the paper argues the real bottleneck in SLM creation isn't training—it's everything around it.

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