Nicholas Arcolano
Bio
Head of AI & Research at Jellyfish, building AI agents and data platforms that help software engineering organizations measure and navigate AI transformation at scale. Leveraging massive real-world data sets to study what's working (and what's not) about AI tool and agent use across the industry, and sharing these learnings through published research and benchmarks so engineering leaders can make confident, evidence-based decisions. Harvard Ph.D., previously at TrueMotion (CMT), Runkeeper, MIT Lincoln Laboratory.
Session (1)
Nicholas Arcolano is Head of Research at Jellyfish, where he leads the largest ongoing industry benchmark study of AI coding tools — drawing on tens of millions of pull requests across hundreds of companies to produce rigorous, data-driven findings on AI adoption rates, productivity gains, and the agentic barrier. Attendees interested in empirical, large-scale evidence on how AI coding tools actually perform in production will find his sessions uniquely grounded in real-world data.
Recent talks (2)
GitHub
@arcolanoRecent writing (6)
- The AI Divide Is Growing: What 37 Million PRs Reveal About Engineering's Future · blog · Apr 2026
- Is tokenmaxxing cost effective? New data from Jellyfish explains. · blog · Apr 2026
- New Research from Harvard and Jellyfish: AI Is Making Developers Faster. So Where's the Business Impact? · blog · Jan 2026
- What Data from 20 Million Pull Requests Reveal About AI Transformation · blog · Dec 2025
- Better Code, or Just Bigger? AI-Assisted Pull Requests Are 18% Larger · blog · Sep 2025
- Jellyfish and OpenAI Team Up to Measure the Impact of AI Coding Tools · blog · Aug 2025
Public activity researched automatically · as of Jun 2026