Benefit = (Volume × Quality) / Time
Measure AI-building impact as experimentation volume times quality, divided by time to launch
- Difficulty
- Easy
- Time to result
- ~months to results
- Steps
- 2
- Confidence
- 88%
A simple metric for evaluating whether an AI-native building transformation is working. Rather than vibes, quantify the benefit as the volume of experiments multiplied by their quality, divided by the time it takes to move from idea to launch. Quality and time reinforce each other.
Origin
Offered by Tomer Cohen (LinkedIn CPO) as the yardstick LinkedIn uses to judge the full-stack builder pilot.
Core principles
- 01More shots at goal matters, but only alongside quality and speed
- 02Quality and time help each other — higher quality means less time spent fixing
- 03Track hours saved per week per role as a leading indicator
- 04Success is never in the launch itself — it's in the iteration the formula unlocks
How to run it
- 1
Count experimentation volume and quality
Track how many experiments teams run and how good they are — richer insights, sharper discussions, better specs — as the numerator of the benefit.
- 2
Divide by idea-to-launch time
Measure the time it takes to pull an experiment from idea to launch and use it as the denominator; watch hours-saved-per-week per role (PMs, designers, engineers) as an early proxy.
Pro tip Expect quality and time to compound — high quality reduces the time you have to spend, improving both terms at once.
Watch out Early on you'll see gains mostly from a small set of top-talent early adopters, not yet a high percentage of the org.
In the wild
During the pilot, LinkedIn saw PMs, designers, and engineers each saving hours of work a week via the analyst agent, rapid prototyping, and the product-jam experience, with insights and discussions measurably better.
→ Early evidence of both a higher numerator (volume × quality) and lower denominator (time), concentrated among top-talent early adopters.
Common mistakes
Judging the transformation by launches instead of iteration
Success never shows up in the launch itself but in the iteration the formula enables; measuring launches alone misses where the value is created.
Expecting whole-org top-line numbers before GA
Early gains come from a small set of top-talent early adopters, so reading org-wide productivity too soon understates the eventual impact.
Is it for you?
Best for
Leaders piloting an AI-building program who need a concrete way to judge whether it's paying off
Not ideal for
Teams wanting a single vanity metric — this requires tracking volume, quality, and time together
From the transcript
“the way I think about the benefits is a function of experimentation volume multiplied by quality. How How good are those experiment experiments divided by…”
“quality and time sometimes they help each other because it's high quality you don't have to spend as much as much time on something”
“we're seeing whether it's PMs, designers, engineers uh saving hours of work a week right now”
“You never see success in the launch itself”
From the episode
Why LinkedIn is turning PMs into AI-powered "full stack builders”
Tomer Cohen (LinkedIn CPO)