Metrics

Three out of four developers now use AI coding assistants daily, yet the Faros AI Engineering Report tracked 22,000 developers across 4,000 teams and found no measurable improvement in DORA metrics at the organizational level. The individual experience of speed clashes directly with what the data shows — and understanding why that gap exists is the first step to closing it. The Numbers Don’t Lie: 75% Adoption, Near-Zero Org-Level Gains The AI productivity paradox is the documented gap between high AI tool adoption rates and flat or negative organizational productivity outcomes. The Faros AI Engineering Report 2026 — the largest dataset of its kind, covering 22,000 real developers across 4,000 teams over two years — found that while 75% of developers actively use AI coding assistants, the majority of organizations recorded no measurable performance gains on standard DORA metrics (deployment frequency, change failure rate, lead time, mean time to recovery). Separately, a 2026 NBER survey of 6,000 executives found that over 80% of individual firms report no measurable AI productivity gains — despite heavy tooling investment. These numbers mirror the “IT Productivity Paradox” that Nobel economist Robert Solow described in the 1980s: “You can see the computer age everywhere except in the productivity statistics.” The analogy is not casual — the IT boom eventually did produce a measurable surge in output growth, but it took roughly 10–15 years to materialize (1995–2004). The question for 2026 is whether AI adoption is following the same delayed curve, or whether structural differences in how software is built are creating a permanent drag that won’t self-correct. ...