The Field Guide to Measuring Token Efficiency in AI Engineering
Three outcome signals. Eleven guardrail metrics. The measurement framework for engineering leaders who need to connect token spend to shipped outcomes and know what to keep, scope, or cut.
AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.
Read the report to uncover what’s holding teams back—and how to fix it fast.
Read the report to uncover what’s holding teams back—and how to fix it fast.
Discover the Engineering Productivity Handbook
How to build a high-impact program that drives real results.
What to measure and why it matters.
And the 5 critical practices that turn data into impact.
What to measure and why it matters.
And the 5 critical practices that turn data into impact.
AI ENGINEERING REPORT 2026
The Acceleration
Whiplash
The definitive data on AI's engineering impact. What's working, what's breaking, and what leaders need to do next.
- Engineering throughput is up
- Bugs, incidents, and rework are rising faster
- Two years of data from 22,000 developers across 4,000 teams
Token Intelligence: The missing operating layer for AI
Token intelligence turns raw AI usage into operational context for engineering, finance, and leadership. Here's what it is, why it matters, and how to build it.
Introduction to Token Intelligence: trace what your AI spend is actually producing
Faros introduces Token Intelligence. Trace every AI token to the work it produced, classify spend by efficiency, and decide which tools and models to keep, scope, or cut. The first step from tokenmaxxing to outcome maxxing.
