How to Resolve the Productivity Paradox in AI-Assisted Coding
February 24 | 10 a.m. PT / 1 p.m. ET / 6 p.m. GMT
Ben GoodTech Lead, Cloud Demo Platform at Google
Austin ParkerDirector of AI Strategy at Honeycomb
AI coding assistants are widely perceived to boost individual developer productivity and code quality. Yet the latest DORA (DevOps Research and Assessment) report reveals a paradox: while engineers rate AI as highly effective for themselves, organizations show far less trust in AI-generated code and struggle to adopt it confidently at scale.
Join Ben Good (Technical Lead, Cloud Demo Platform at Google) and Austin Parker (Director of AI Strategy at Honeycomb) as they unpack why that gap exists and why it has less to do with AI models and more to do with how teams learn from production.
What we’ll cover
- Why engineers trust their AI-assisted changes more than anyone else’s
- What the DORA report reveals about individual vs. organizational confidence in AI
- Why “trust” is the wrong abstraction for AI-generated code
- How elite teams replace trust with fast, production-grade feedback loops
- 🔥AMA: Live AMA where our speakers will address your most burning questions. Submit your questions when you register, or bring them live!