Researcher at Stanford University leading groundbreaking studies on software engineering productivity. Analyzed 120,000+ engineers across hundreds of companies to quantify AI’s real impact on developer work. Former Chief of Staff at DHL leading digital transformation for 6,000+ engineers. Olympic Weightlifting National Champion and Stanford MBA (merit scholar).
Data-Driven Authority on AI ROI
Yegor Denisov-Blanch provides the most comprehensive, data-backed evidence on AI’s impact on developer productivity—analyzing actual code from hundreds of companies rather than surveys or self-reports. His research reveals uncomfortable truths about AI effectiveness and developer performance.
Current Research
At Stanford’s Software Engineering Productivity Research Group, Yegor leads empirical studies analyzing private Git repositories from 100,000+ engineers across nearly 1,000 companies. His work quantifies:
- AI ROI Variance: Productivity gains range from 35-40% in greenfield work to 0-10% in brownfield/high-complexity scenarios
- Ghost Engineers: 9.5% of software engineers contribute virtually nothing (14% remote, 9% hybrid, 6% office-based)
- Code Quality Multiplier: Clean code amplifies AI gains; poor code quality eliminates benefits (“rich get richer effect”)
- AI Maturity Framework: L0-L4 classification correlating organizational AI adoption with achieved productivity gains
Background
Stanford MBA (school’s only merit-based scholarship recipient). 8th-grade dropout who became sole family breadwinner after mother’s cancer diagnosis. Led digital transformation for 6,000+ engineers at DHL as Chief of Staff to CEO. Olympic Weightlifting National Champion and Master of Sport. Unique blend of technical research, business strategy, and enterprise transformation experience.
Key Publications
- Predicting Expert Evaluations in Software Code Reviews - ML model automating code review with r=0.82-0.86 correlation to human experts
- Measuring Determinism in LLMs for Code Review - Consistency evaluation of GPT-4o, Claude 3.5, LLaMA on code tasks
Research Tools & Talks
- p10y Platform - Productivity measurement and benchmarking tool based on Stanford research
- AI Impact Framework - Assessing real-world AI tool effectiveness
- DPE Summit 2024 Talk - Measuring productivity in development environments
- YouTube: How to Quantify AI ROI - Conference presentation with real data
Key Research Insights
Context-Dependent Impact: AI productivity gains are highly contextual. Greenfield work sees 35-40% improvements while legacy code refactoring sees only 0-10% gains.
Quality Over Quantity: AI usage quality matters more than volume. Token consumption is not a reliable success metric—strategic, focused usage outperforms high-volume unfocused usage.
Environment Cleanliness Index: Clean, well-maintained code is a prerequisite for AI effectiveness. Top-performing teams see compounding benefits while struggling teams face diminishing returns (“rich get richer effect”).
Flawed Traditional Metrics: Conventional measurements (lines of code, story points, DORA metrics, commit counts) don’t accurately measure engineering productivity and may encourage counterproductive behaviors.
Methodology: ML models replicate expert panel evaluations. Time-series analysis of GitHub data accounts for rework, refactoring, and code quality. Integration with telemetry from enterprise tools like Cursor Enterprise.
Organizational Implications: Successful AI adoption requires investment in software quality and engineering practices, not just tool acquisition. Agents should focus on “fighting entropy” and cleaning codebases to amplify AI gains.