WhatA tool that sits on top of version control and CI/CD to measure real throughput — features shipped, bugs introduced, cycle time — rather than vanity metrics like commits or lines changed, specifically calibrated for AI-augmented teams.
SignalMultiple engineers report that commit volume and code output have surged but actual software delivery speed to end users and ops teams hasn't meaningfully improved, suggesting managers are flying blind on whether AI tooling is actually working.
Why NowCompanies are spending heavily on AI coding tool licenses and token budgets without any way to measure ROI, and the gap between perceived and actual productivity is widening as AI-generated commit noise grows.
MarketEngineering leaders at mid-to-large software companies (50+ devs); $5B+ engineering intelligence market; LinearB, Jellyfish, and DX exist but none isolate AI-assisted vs. human contributions or measure AI tool ROI directly.
MoatDeep integration into the dev toolchain (git, CI, ticketing) creates high switching costs, and longitudinal baseline data becomes irreplaceable once established.
Labor market impacts of AI: A new measure and early evidenceView discussion ↗ · Article ↗ · 332 pts · March 5, 2026
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