Description

Engineering teams are shipping twice as many pull requests with AI — but merge rates on AI-generated PRs have dropped from 80% to 60%. 

Nick Arcolano, Head of AI & Research at Jellyfish, sits on one of the most comprehensive datasets in the industry: 250,000 developers, 40 million data points, monthly benchmarks on real agentic coding adoption across enterprise companies. What he's seeing in that data is both more promising and more complicated than the headlines suggest. 

What we cover: 

• Why experienced engineers hit a hard ceiling at 4 concurrent agents, and what it would take to break through it 
• The 80/20 vs 60/40 merge rate gap between human and AI-generated pull requests — and what's actually causing it
• How AI adoption reached 71% weekly active usage across 250K developers, and what "depth of use" really means
• Why 2026 is the year the CFO gets involved — and how engineering leaders should prepare to show their receipts
• The biggest misconception engineering leads have about what it takes to get to true AI-native development
• Why companies have jet engines but are still building cars, and what the real architectural changes look like 

Links: 

🌐 Tessl: https://tessl.io 

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If you're an engineering leader trying to make sense of the gap between the AI hype and what's actually showing up in production, drop your take in the comments.

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