Original data on AI-native software delivery

Engineering burnout has risen across the 2019–2024 window (39%→62%→53% in the Yerbo cycle; 41% Stack Overflow 2024). Almost no engineering survey uses the validated MBI. The literature points strongly at autonomy and process fit; process debt is rarely measured. Volume 0 reads the clinical literature; Volume 1 (Q3 2026) lands the 600-engineer study.

Seven years of DORA Accelerate data converge on four metrics that predict org performance — and one repeated misinterpretation of what they prove. DORA explicitly does NOT claim causation; the popular reading routinely ignores that. Volume 0 reads the literature; Volume 1 (Q4 2026) lands the AI-era segmented refresh.

Software estimates have been ~30% optimistic on average for 43 years (Halkjelsvik & Jørgensen 2012). The 2026 question isn't whether AI fixes that — it's whether AI hides it. Volume 0 synthesises the literature and pre-registers Stride's 500-person calibration study; Volume 1 lands at this URL in Q4 2026.

AI adoption in software delivery is universal (~76–95% across DORA, Stack Overflow, McKinsey). Productivity findings disagree wildly — Microsoft/GitHub measured +55% on standardised tasks; METR measured −19% on real OSS work, with the same developers feeling +20% faster. The Stride 2026 study (n≥1,500, pre-registered) closes the measurement gap. Headline findings: July 2026.