A consented cohort-benchmarking product that compares versioned engineering workflow measures before and after AI adoption with coverage, uncertainty, and privacy controls.
Genesis score7.23/10
Make Devbenchmark real.0/500
500 more votes and Devbenchmark is authorized for build.
0%500 to authorize
Backing is the vote. When an idea crosses 500, we pull it into the build pipeline and ship it for real — the votes decide what gets built next, not an editor.
The opportunity
2011–presentPublic archive coverage
0Direct cross-platform peer found
0Individual productivity ranks
The case
Engineering leaders want to know whether AI changes delivery, quality, review, learning, and rework, but public repository activity and assessment scores do not establish productivity or causality. Devbenchmark builds comparable cohort definitions, reports distributions and uncertainty, and separates organizational observations from experimental claims. It never ranks individual developers, infers work from public activity alone, transfers assessment performance into job value, or promises that a pre-AI baseline is an equivalent counterfactual.
Who pays — and why
Engineering executives, developer-experience leaders, people analytics teams, and researchers evaluating organization-level workflow change.
What it unlocks
Versioned cohort definitions for role, tenure, repository type, language, work mix, team, period, and AI-adoption exposure
Organization-authorized measures of cycle time, review, rework, defects, incidents, delivery, learning, and developer-reported experience
Public baseline research with explicit population, missing context, bots, repository selection, and comparability limitations
Privacy-preserving benchmark distributions with minimum cohorts, suppression, confidence intervals, drift, and no individual ranking
How Genesis scored it
7.23across seven criteria
8
Temporal window
AI adoption creates immediate demand for before-and-after evidence.
8
Buyer persona
Engineering and developer-experience leaders are concrete.
5
Convergence
Three cross-references and four inbound links show useful raw echo.
Why it scored well
A clear engineering-research buyer, long public event history, free skill taxonomies, and no cross-platform benchmark peer found support the direction.
What's holding it back
Assessment partnerships are unverified, productivity constructs are contested, public data is nonrepresentative, privacy risk is high, and incumbents can build benchmarks.
Signals detected4 sources crossed
Signalofficial data research carried in Genesis
Signalofficial interface research carried in Genesis
SignalGenesis market scan
SignalGenesis scoring audit
Direction briefdevbenchmark.md
devbenchmark.md
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