Spendassay Research

What the evidence actually says.

Spendassay publishes the research its method rests on, including the findings that cut against us. Every study is linked to its primary source and reported with its effect size, so you can check the claim rather than take ours.

What Spendassay Research publishes

  • Evidence reviews

    Vendor-neutral reads of the published research, effect sizes included, and the findings that argue against us reported alongside the ones that do not.

  • Benchmarks

    Citable reference tables. The load-bearing column is the basis for each row, because a benchmark without a population behind it is an anecdote with a number on it.

  • The AI tool price index

    Published prices for every tool in the catalog, each stamped with the date it was last read against the vendor's own page. Where no price is published, we say so.

  • Methodology

    How every number is built, layer by layer, and how sure each one is. The standard the rest of this work is held to.

The byline is the company, not a persona — how that work is produced, and who runs it.

Reviews

  • Does AI actually make developers faster?

    The published evidence, linked and dated, with effect sizes and a comparison table. Includes the results that cut against the case for AI tooling, because a review that only cites the favourable studies is marketing.

The sources we cite

8 published studies underpin the method. Each is listed with its finding on the methodology page, and compared side by side in the review above.

SourceYearMeasures
METR2025Task completion time, with and without AI tools
Faros AI2026Delivery quality against AI-accelerated throughput
GitClear2025Code churn and duplication over time
DORA2025DORA delivery and stability metrics under AI adoption
CACM · Ziegler et al.2024Perceived productivity vs. acceptance rate
LinearB · APEX2026Adoption curve and payback window
Microsoft · Viva2026Minimum group size for reporting
DX (now part of Atlassian)2026Longitudinal engineering velocity across dimensions

Measure your own numbers, not an industry average.

The research says the effect varies by team. That is the argument for measuring yours.