Better AI Models Won't Fix Your Engineering Team — Nicholas Arcolano, Jellyfish
AI underperformance in engineering teams reflects process failures, not model limitations
“if you're not getting the results you want with AI, um, the problem is probably not with the model, it's with you”
Jellyfish's Head of AI Nicholas Arcolano argues that engineering teams blaming AI model quality for poor outcomes are misdiagnosing the problem — the bottleneck is workflow and team behavior, not model capability. Drawing on Jellyfish's dataset of 1,300 companies and hundreds of thousands of developers, he contends that model improvements are real but insufficient without organizational readiness. The talk reframes the AI productivity debate from 'which model' to 'how teams adopt and integrate AI tools.'